WO2023181418A1

WO2023181418A1 - Regulation device for regulating control parameter, control system, and control parameter regulation method

Info

Publication number: WO2023181418A1
Application number: PCT/JP2022/014696
Authority: WO
Inventors: 瑶梁
Original assignee: ファナック株式会社
Priority date: 2022-03-25
Filing date: 2022-03-25
Publication date: 2023-09-28

Abstract

In the present invention, a plurality of frequency characteristics for when a control parameter has been regulated by a plurality of regulation conditions is determined by a measurement of one instance of a frequency characteristic.　The present invention comprises: a frequency characteristic storage unit that stores a frequency characteristic of a machine, where the frequency characteristic is measured by operating a motor control unit having a pre-regulation control parameter; a regulation condition recording unit that records a plurality of regulation conditions for regulating the control parameter; a frequency characteristic prediction unit that uses the pre-regulation and post-regulation control parameters and the stored frequency characteristic to predict a post-regulation frequency characteristic for the control parameter; a control parameter regulation unit that uses one of the plurality of recorded regulation conditions and the predicted frequency characteristic to regulate the control parameter of the frequency characteristic prediction unit in order to optimize the control parameter; and a control parameter setting unit that, in the motor control unit, sets a control parameter selected from a plurality of the control parameters.

Description

Adjustment device, control system, and control parameter adjustment method for adjusting control parameters

The present invention relates to an adjustment device that adjusts control parameters of a motor control device that controls a motor, a control system including the adjustment device, and a control parameter adjustment method.

Control is used when optimizing control parameters such as motor gain and filter coefficients that satisfy preset stability conditions (stability margin, etc.) for motors used to drive machines such as machine tools, robots, or industrial machinery. Set the parameters, operate the motor control device to measure the frequency characteristics of the machine, adjust the control parameters, operate the motor control device and remeasure the frequency characteristics to check whether stability conditions are met. It was necessary to perform a series of adjustment processes multiple times.

Patent Document 1 describes a control parameter sensitivity analysis device for checking the adjustment results of control parameters in order to optimally adjust the control parameters of a motor control device used in semiconductor manufacturing equipment, machine tools, or industrial robots. has been done.
Patent Document 1 discloses that a control parameter sensitivity analysis device is a motor control device that includes a detection means that detects the amount of operation of a machine, a command device that generates a command signal, and a controller that drives a motor in response to the command signal. , an open-loop frequency response characteristic measuring means for obtaining an open-loop frequency response characteristic that does not include controller characteristics, a control model of a controller of a motor control device, and a closed-loop frequency measurement means for obtaining an open-loop frequency response characteristic that does not include controller characteristics; It is described that the apparatus includes a calculation means for calculating a response characteristic, and a sensitivity analysis device for performing sensitivity analysis on the relationship between a control parameter of a controller and a change in a closed-loop frequency response characteristic.

A control support device that adjusts at least one of the coefficients of at least one filter and the feedback gain (FG) (which serve as a control parameter) of a servo control unit (which serves as a motor control unit) that controls a motor is disclosed in Patent Document 2. It is described in 2.
Patent Document 2 discloses that a control support device provides first information and second information including at least one of a feedback gain and a coefficient of at least one filter of a servo control device that controls a motor before and after adjustment. is used to calculate the frequency characteristics of at least one of the frequency characteristics of the input/output gain and phase delay between the filter and the feedback gain before and after adjusting at least one of the filter coefficient and feedback gain, and At least one of the coefficient and feedback gain of the filter is determined based on the frequency characteristic and the measured frequency characteristic of the input/output gain and the input/output phase delay of the servo control device before adjustment of at least one of the coefficient and the feedback gain. It is described that estimated values of the frequency characteristics of the input/output gain and phase delay of the servo control device after adjustment are obtained.

JP2005-275588A International Publication No. 2021/251226

When setting a stability margin as a stability condition for a motor control device, if the stability margin (referring to gain margin and phase margin) is large, stability increases, but responsiveness decreases. On the other hand, when the stability margin is small, stability decreases but responsiveness increases.
When setting the stability margin for a motor control device, the user must check how the frequency characteristics of the motor control device change under multiple adjustment conditions, such as standard, stability-oriented, responsiveness-oriented, and custom conditions. You may want to determine the adjustment conditions depending on the situation.
However, it takes a lot of time to repeat the process of adjusting control parameters such as motor gain and filter coefficients and determining frequency characteristics for each of a plurality of adjustment conditions.
Therefore, it is desired to be able to confirm the results of adjusting the control parameters of the motor control device under a plurality of adjustment conditions by measuring the frequency characteristics once.

(1) A first aspect of the present disclosure is an adjustment device that adjusts control parameters of a motor control unit that controls a motor,
a frequency characteristic storage unit that stores frequency characteristics of the machine measured by operating the motor control unit having control parameters before adjustment;
an adjustment condition setting unit that sets a plurality of adjustment conditions for adjusting the control parameters of the motor control unit;
a frequency characteristic prediction unit that predicts the frequency characteristic of the machine after the control parameter is adjusted, using the control parameter before and after adjustment, and the frequency characteristic stored in the frequency characteristic storage unit;
Adjusting the control parameters input to the frequency characteristic prediction unit in order to optimize the control parameters using the predicted frequency characteristics and one of the plurality of adjustment conditions set by the adjustment condition setting unit. a control parameter adjustment section,
a control parameter storage unit that stores the plurality of control parameters optimized for the plurality of adjustment conditions;
an evaluation index calculation unit that calculates an evaluation index of the frequency characteristic from the predicted frequency characteristic corresponding to the optimized control parameter;
a presentation unit that presents at least one of the predicted frequency characteristics and the evaluation index corresponding to the optimized control parameters for each adjustment condition of the plurality of adjustment conditions;
a control parameter setting unit that sets a control parameter selected from the plurality of control parameters stored in the control parameter storage unit to the motor control unit;
It is an adjustment device equipped with.

(2) A second aspect of the present disclosure is an adjustment device that adjusts control parameters of a motor control unit that controls a motor,
a frequency characteristic storage unit that stores frequency characteristics of the machine measured by operating the motor control unit having control parameters before adjustment;
an adjustment condition setting unit that sets a plurality of adjustment conditions for adjusting the control parameters of the motor control unit;
a frequency characteristic prediction unit that predicts the frequency characteristic of the machine after the control parameter is adjusted, using the control parameter before and after adjustment, and the frequency characteristic stored in the frequency characteristic storage unit;
Adjusting the control parameters input to the frequency characteristic prediction unit in order to optimize the control parameters using the predicted frequency characteristics and one of the plurality of adjustment conditions set by the adjustment condition setting unit. a control parameter adjustment section to
a control parameter storage unit that stores the plurality of control parameters optimized for the plurality of adjustment conditions;
a time response prediction unit that predicts the first time response using the predicted frequency characteristics corresponding to the optimized control parameters;
an evaluation index calculation unit that calculates an evaluation index of the first time response from the predicted first time response;
a presentation unit that presents at least one of the first time response and the evaluation index for each adjustment condition of the plurality of adjustment conditions;
a control parameter setting unit that sets a control parameter selected from the plurality of control parameters stored in the control parameter storage unit to the motor control unit;
It is an adjustment device equipped with.

(3) A third aspect of the present disclosure is a control system including a motor control unit that controls a motor, and the adjustment device of (1) or (2) above.

(4) A fourth aspect of the present disclosure is a control parameter adjustment method for adjusting control parameters of a motor control unit that controls a motor, the method comprising:
The computer is
a process of saving the frequency characteristics of the machine measured by operating the motor control unit having the control parameters before adjustment;
a process of setting a plurality of adjustment conditions for adjusting the control parameters of the motor control unit;
A process of predicting a frequency characteristic of the machine after adjusting the control parameter using the control parameter before and after adjustment and the saved frequency characteristic;
A process of adjusting the control parameter in order to optimize the control parameter using the predicted frequency characteristic and one of the plurality of set adjustment conditions;
a process of storing the plurality of control parameters optimized for the plurality of adjustment conditions;
A process of calculating an evaluation index of the frequency characteristic from the predicted frequency characteristic corresponding to the optimized control parameter;
a process of presenting at least one of the predicted frequency characteristics and the evaluation index corresponding to the optimized control parameters for each adjustment condition of the plurality of adjustment conditions;
a process of setting a control parameter selected from the plurality of stored control parameters in the motor control unit;
This is a control parameter adjustment method that executes.

(5) A fifth aspect of the present disclosure is a control parameter adjustment method for adjusting control parameters of a motor control unit that controls a motor, the method comprising:
The computer is
a process of saving the frequency characteristics of the machine measured by operating the motor control unit having the control parameters before adjustment;
a process of setting a plurality of adjustment conditions for adjusting the control parameters of the motor control unit;
A process of predicting a frequency characteristic of the machine after adjusting the control parameter using the control parameter before and after adjustment and the saved frequency characteristic;
A process of adjusting the control parameter in order to optimize the control parameter using the predicted frequency characteristic and one of the plurality of set adjustment conditions;
a process of storing the plurality of control parameters optimized for the plurality of adjustment conditions;
a process of predicting a first time response using predicted frequency characteristics corresponding to the optimized control parameters;
a process of calculating an evaluation index of the first time response from the predicted first time response;
a process of presenting at least one of the first time response and the evaluation index for each adjustment condition of the plurality of adjustment conditions;
a process of setting a control parameter selected from the plurality of stored control parameters in the motor control unit;
This is a control parameter adjustment method that executes.

According to each aspect of the present disclosure, it is possible to obtain multiple frequency characteristics when control parameters such as the gain of the motor control unit and the coefficient of the filter are adjusted under multiple adjustment conditions by measuring the frequency characteristics once. . As a result, by checking multiple frequency characteristics and/or evaluation indices of multiple frequency characteristics, you can easily compare the frequency characteristics and/or evaluation indices of frequency characteristics after adjustment under different adjustment conditions, and apply the control you want to apply. Parameters can be easily selected. In addition, by checking multiple time responses and/or multiple time response evaluation metrics predicted from multiple frequency characteristics, you can easily evaluate the time response and/or time response evaluation metrics after adjustment under different adjustment conditions. You can easily select the control parameters you want to apply.

FIG. 1 is a block diagram showing a control system according to a first embodiment of the present disclosure. FIG. 3 is a Bode diagram showing a gain margin, a phase margin, a maximum gain of closed-loop characteristics, and a maximum gain of a high frequency region. FIG. 2 is a block diagram showing an example of the configuration of an adjustment section. It is a figure which shows an example of the setting screen of adjustment conditions. FIG. 2 is a diagram showing a complex plane showing a unit circle on the complex plane and two circles forming a closed curve. It is. FIG. 3 is a diagram showing a Nyquist locus, a unit circle, and a circle passing through a gain margin and a phase margin drawn on a complex plane. Display displaying Bode diagrams of open-loop frequency characteristics or closed-loop frequency characteristics for "before adjustment", "standard", and "stability emphasis", and evaluation indicators regarding "before adjustment", "standard", and "stability emphasis" It is a figure which shows a screen. It is a Bode diagram shown in display field 502A. It is a Bode diagram shown in display field 502B. It is a Bode diagram shown in display field 502C. It is a Bode diagram showing open-loop frequency characteristics or closed-loop frequency characteristics regarding "Standard", "Stability Emphasis", "Responsivity Emphasis", and "Custom". It is a figure which shows the setting screen of adjustment conditions. 5 is a flowchart showing the operation of the adjustment section. It is a block diagram showing a modification of the adjustment section in which the control parameter adjustment section is replaced with a machine learning section. FIG. 2 is a block diagram showing the configuration of a machine learning section. FIG. 2 is a block diagram showing a closed-loop reference model. FIG. 7 is a characteristic diagram showing the frequency characteristics of input/output gains of the motor control section of the reference model, and the motor control sections before and after learning. FIG. 2 is a block diagram illustrating a configuration example of an adjustment section included in a control system according to a second embodiment of the present disclosure. It is a Bode diagram showing a first resonance mode and a second resonance mode. FIG. 7 is a diagram showing a display screen displaying time responses regarding "before adjustment," "standard," and "emphasis on stability," and evaluation indicators regarding "before adjustment," "standard," and "emphasis on stability." It is a figure which shows the characteristic of the time response shown in 702 A of display columns. It is a figure which shows the characteristic of the time response shown in the display field 702B. It is a figure which shows the characteristic of the time response shown in display field 702C. FIG. 2 is a block diagram showing an example of a filter configured by directly connecting a plurality of filters. FIG. 2 is a block diagram showing another configuration example of the control system.

Hereinafter, embodiments of the present disclosure will be described in detail using the drawings.
(First embodiment)
FIG. 1 is a block diagram showing a control system according to a first embodiment of the present disclosure.
The control system 10 includes a motor control section 100, a frequency generation section 200, a frequency characteristic measurement section 300, and an adjustment section 400. The motor control section 100 corresponds to a motor control device, and the adjustment section 400 corresponds to an adjustment device.
Note that one or more of the frequency generation section 200, the frequency characteristic measurement section 300, and the adjustment section 400 may be provided within the motor control section 100. The frequency characteristic measuring section 300 may be provided within the adjusting section 400.

The motor control section 100 includes a subtracter 110, a speed control section 120, a filter 130, a current control section 140, and a motor 150. The subtracter 110, the speed control section 120, the filter 130, the current control section 140, and the motor 150 constitute a closed speed feedback loop servo system. As the motor 150, a linear motor that performs linear motion, a motor that has a rotating shaft, or the like can be used. The object driven by the motor 150 is, for example, a mechanical part of a machine such as a machine tool, a robot, or an industrial machine. The motor 150 may be provided as part of a machine tool, a robot, an industrial machine, or the like. The control system 10 may be provided as part of a machine tool, a robot, an industrial machine, or the like. The details of the configuration of the motor control section 100 will be described later.

The frequency generation unit 200 outputs a sine wave signal as a speed command to the subtracter 110 and the frequency characteristic measurement unit 300 of the motor control unit 100 while changing the frequency.

The frequency characteristic measurement unit 300 detects a speed command (sine wave) as an input signal generated by the frequency generation unit 200 and a detection signal as an output signal output from a rotary encoder (not shown) provided on the motor 150. Using the velocity (sine wave) or the integral of the detected position (sine wave) that becomes the output signal output from the linear scale, the amplitude ratio (input The frequency characteristics of output gain) and phase delay are determined and output to adjustment section 400. The obtained frequency characteristic is a closed-loop frequency characteristic Pc.
Furthermore, the frequency characteristic measurement section 300 calculates an open loop frequency characteristic Po from this frequency characteristic Pc, and outputs it to the adjustment section 400. The closed loop frequency characteristic Pc is expressed as Pc=Po/(1+Po) using the open loop frequency characteristic Po. Therefore, the open loop frequency characteristic Po can be determined by Po=Pc/(1-Pc).

The adjustment unit 400 adjusts the gain of one or both of the integral gain K1v and the proportional gain K2v of the speed control unit 120, and the coefficients ω _c , τ, of the transfer function of the filter 130, for each adjustment condition of the plurality of adjustment conditions. The optimum value of at least one of δ (which becomes a control parameter) is determined. The plurality of adjustment conditions are, for example, two or more adjustment conditions of standard, stability-oriented, responsiveness-oriented, custom, etc., which are classified according to the characteristics of the frequency response. "Standard" is an intermediate setting between emphasis on stability and emphasis on responsiveness, and "Custom" is set arbitrarily by the user.
Each adjustment condition is a condition that places a limit on at least one of the gain margin, the phase margin, the maximum gain of the closed loop characteristic, and the maximum gain of the high frequency region.
FIG. 2 is a Bode diagram showing a gain margin, a phase margin, a maximum gain of closed-loop characteristics, and a maximum gain in a high frequency region.
Table 1 shows examples of limit values for gain margin, phase margin, maximum gain of closed-loop characteristics, and maximum gain in high frequency region in standard, stability-oriented, responsiveness-oriented, and custom.

For each adjustment condition, the adjustment unit 400 displays the frequency characteristics of the machine under the determined optimal control parameters, or calculates and displays evaluation indicators such as gain margin, phase margin, control band, etc. of the frequency characteristics. Then, the adjustment unit 400 allows the user to select a frequency characteristic or an evaluation index from a plurality of frequency characteristics or a plurality of evaluation indices displayed for each adjustment condition, and select an optimal frequency characteristic or evaluation index corresponding to the selected frequency characteristic or evaluation index. The control parameters, i.e., one or both of the integral gain K1v and the proportional gain K2v of the speed control unit 120, and the optimum value of at least one of the coefficients ω _c , τ, and δ of the transfer function of the filter 130 are set to the motor. The settings are made in the control unit 100.

The motor control section 100 and adjustment section 400 will be explained in more detail below.
<Motor control unit 100>
As already explained, the motor control section 100 includes a subtracter 110, a speed control section 120, a filter 130, a current control section 140, and a motor 150.

The subtracter 110 calculates the difference between the input speed command and the detected speed fed back, and outputs the difference to the speed control unit 120 as a speed deviation.

The speed control unit 120 performs PI control (Proportional-Integral Control), adds the value obtained by multiplying the speed deviation by an integral gain K1v and integrating the value, and the value obtained by multiplying the speed deviation by a proportional gain K2v, and outputs the result as a torque command. Output to filter 130. Speed control section 120 includes a feedback gain. Note that the speed control unit 120 is not particularly limited to PI control, and may use other control, such as PID control (Proportional-Integral-Differential Control).
Equation 1 (shown as Equation 1 below) represents a transfer function G _V (s) of the speed control section 120.

The filter 130 is a filter that attenuates specific frequency components, such as a notch filter, a low-pass filter, or a bandstop filter. In a machine such as a machine tool that has a mechanical section driven by the motor 150, a resonance point exists, and resonance may increase in the motor control section 100. Resonance can be reduced by using a filter such as a notch filter. The output of filter 130 is output to current control section 140 as a torque command.
Equation 2 (shown as Equation 2 below) represents a transfer function G _F (s) of the notch filter as the filter 130. The parameters indicate coefficients ω _c , τ, and δ.
In Equation 2, the coefficient δ is the damping coefficient, the coefficient ω _c is the central angular frequency, and the coefficient τ is the fractional band. When the center frequency is fc and the bandwidth is fw, the coefficient ω _c is expressed as ω _c =2πfc, and the coefficient τ is expressed as τ = fw/fc.

Current control unit 140 generates a current command for driving motor 150 based on the torque command, and outputs the current command to motor 150.
When the motor 150 is a linear motor, the position of the movable part is detected by a linear scale (not shown) provided on the motor 150, and a detected speed value is obtained by differentiating the detected position value, and the detected speed is calculated by differentiating the detected position value. The value is input to subtractor 110 as velocity feedback.
If the motor 150 has a rotating shaft, the rotational angular position is detected by a rotary encoder (not shown) provided on the motor 150, and the detected speed value is input to the subtracter 110 as speed feedback.

<Adjustment section 400>
FIG. 3 is a block diagram showing an example of the configuration of the adjustment section. As shown in FIG. 3, the adjustment section 400 includes a frequency characteristic storage section 401, an adjustment condition setting section 402, a frequency characteristic prediction section 403, a control parameter adjustment section 404, a control parameter storage section 405, an evaluation index calculation section 406, and a presentation section. 407 and a control parameter setting section 408.
Each part of the adjustment section 400 will be explained below.

(Frequency characteristic storage section 401)
The frequency characteristic storage unit 401 stores the closed-loop frequency characteristic Pc and the open-loop frequency characteristic Po of input/output gain and phase delay, which are output from the frequency characteristic measurement unit 300. The closed-loop frequency characteristic Pc is the frequency characteristic of the machine acquired by the frequency characteristic measuring section 300 when the motor control section 100 operates with the control parameters before adjustment.

(Adjustment condition setting section 402)
The adjustment condition setting unit 402 displays a setting screen for inputting adjustment conditions, and sets the gain margin and phase margin of the open loop circuit of the motor control unit 100 for each adjustment condition of the plurality of adjustment conditions input by the user. , the maximum gain of the closed-loop characteristic, and the maximum gain of the high frequency region (stability condition). The gain margin, phase margin, maximum gain of closed loop characteristics, and maximum gain of high frequency region may be set in advance to predetermined values, or may be set arbitrarily by the user. In the following explanation, a case will be explained in which stability margins (gain margin and phase margin) are set.
When a plurality of adjustment conditions are input by the user, the adjustment condition setting unit 402 notifies the control parameter adjustment unit 404 that the plurality of adjustment conditions have been input.

The adjustment condition setting unit 402 outputs image data including a closed curve passing through a gain margin and a phase margin on a complex plane to the control parameter adjustment unit 404 for each adjustment condition based on a request from the control parameter adjustment unit 404. The open loop circuit is comprised of the speed control section 120, filter 130, current control section 140, and motor 150 shown in FIG.
Specifically, the adjustment condition setting unit 402 first displays a setting screen shown in FIG. 4, and the user selects an adjustment condition on the setting screen. The user can adjust two or more of the four adjustment conditions shown in FIG. 4, for example, standard, stability-oriented, responsiveness-oriented, and custom, for cutting feed and rapid feed on the X-axis and Y-axis. Select each. FIG. 4 shows how the user sequentially selects and sets standard and stability-oriented fast forwarding on the Y axis.

Next, the adjustment condition setting unit 402 draws a unit circle whose circumference passes through (-1, 0) on the complex plane of FIG. A closed curve such as a circle that includes (-1, 0) on the plane inside is drawn, and image data including the closed curve is output to the control parameter adjustment unit 404 for each adjustment condition. What is output to the control parameter adjustment unit 404 does not need to be image data, but may be data that shows a closed curve such as a circle on at least a complex plane. In the following description, it is assumed that the adjustment condition setting unit 402 outputs image data to the control parameter adjustment unit 404.

FIG. 5 shows complex planes when the adjustment conditions are "standard" and "stability-oriented".
When the adjustment condition is “standard”, the adjustment condition setting unit 402 draws a circle C2 with a small diameter as a closed curve on a complex plane, and outputs the image data to the control parameter adjustment unit 404. Further, when the adjustment condition is "emphasis on stability", a circle C1 with a large diameter is drawn as a closed curve on a complex plane, and image data is output to the control parameter adjustment unit 404.
The point where the circle C1 or C2 intersects with the real axis determines the gain margin, and the point where the circle C1 or C2 intersects with the unit circle determines the phase margin. As the diameter of the circle increases, the stability margin (referring to gain margin and phase margin) increases, and stability increases, but responsiveness decreases.

In FIG. 5, the centers of the circles C1 and C2 are on the real axis, but they do not have to be on the real axis. The closed curve may be a closed curve other than a circle, such as a rhombus, a quadrilateral, or an ellipse. In FIG. 5, the center of the circle C1 and the circle C2 is the same, but the center of the circle C1 and the center of the circle C2 may be different.

(Frequency characteristic prediction unit 403)
The frequency characteristic prediction unit 403 stores the transfer function G _V (jω) of the speed control unit 120 and/or the transfer function G _F (jω) of the filter 130 using the control parameters before adjustment.
Note that the control parameters before adjustment are generated by the user in advance. If the operator has adjusted the control parameters in advance, the adjusted values may be used as "control parameters before adjustment."
Then, the frequency characteristic prediction unit 403 uses the transfer function G _V (jω) of the speed control unit 120 and/or the transfer function G _F (jω) of the filter 130 using the control parameters before adjustment. The frequency characteristic C ₁ of the input/output gain and phase delay of the filter 120 and/or the filter 130 is calculated. Further, the frequency characteristic prediction unit 403 calculates the transfer function G _V (jω) and/or the speed control unit 120 using the adjusted control parameters that are adjusted based on the adjustment information output from the control parameter adjustment unit 404. Alternatively, the frequency characteristic C ₂ of the input/output gain and phase delay of the speed control unit 120 and/or the filter 130 is calculated using the transfer function G _F (jω) of the filter 130 . The transfer function G _V (jω) of the speed control unit 120 and/or the transfer function G _F (jω) of the filter 130 using the adjusted control parameters is determined by replacing the unadjusted control parameters with the adjusted control parameters. You can get it at

For example, the frequency characteristic when the integral gain K1v and proportional gain K2v of the transfer function G _V (jω) of the speed control unit 120 are multiplied by n (n is an integer) from the value before adjustment is the frequency characteristic before adjustment G _V (jω), then n×G _V (jω). On the Bode diagram, the gain and phase are respectively expressed by Equation 3 (hereinafter shown as Equation 3) and Equation 4 (hereinafter shown as Equation 4).

As described above, the frequency characteristic prediction unit 403 obtains the calculated frequency characteristic C ₁ and frequency characteristic C ₂ .

The frequency characteristic prediction unit 403 further performs the following processing using the calculated frequency characteristic C ₁ and frequency characteristic C ₂ .
The frequency characteristic prediction unit 403 calculates the open loop frequency of the input/output gain and phase delay of the motor control unit 100 based on the frequency characteristic C ₁ , the frequency characteristic C ₂ and the open loop frequency characteristic Po acquired from the frequency characteristic storage unit 401. Find the estimated value Eo of the characteristic.
Specifically, the frequency characteristic prediction unit 403 calculates the estimated value Eo of the open-loop frequency characteristic of the input/output gain and phase lag of the motor control unit 100 using the following equation 5 (shown as equation 5 below). demand.

Further, the frequency characteristic prediction unit 403 calculates the estimated value Ec of the closed-loop frequency characteristic using the estimated value Eo of the open-loop frequency characteristic by Ec=Eo/(1+Eo).
Note that the estimated value Eo of the frequency characteristic of the input/output gain and phase delay of the motor control section 100 can be calculated using the above formula 5, that is, Eo = C ₂ - C ₁ + Po, but the frequency characteristic prediction section 403 The calculation to be performed may be Eo=(C ₂ −C ₁ )+Po, Eo=(Po−C ₁ )+C ₂ , or E=(Po+C ₂ )−C ₁ .
A case in which the frequency characteristic prediction unit 403 calculates the estimated value Eo of the open-loop frequency characteristic using the formula Eo=(C ₂ -C ₁ )+Po will be further described below.

For example, as described above, when the integral gain K1v and proportional gain K2v of the transfer function G _V (jω) of the speed control unit 120 are multiplied by n (n is an integer) from the value before adjustment, the frequency characteristic _C1 is the initial value. If the frequency characteristic C ₂ of the state is G _V (jω), then n×G _V (jω) is obtained.
The frequency characteristic of the gain when the initial state is multiplied by n, shown in the Bode diagram, is expressed by adding 20log _{10 (n) to the initial state gain, 20log 10} _| G _V (jω)|, as shown in Equation 4. It becomes the added frequency characteristic. The frequency characteristic of the phase when multiplied by n from the initial state shown in the Bode diagram is tan ⁻¹ (n)=0, so as shown in Equation 4, it is not different from the phase frequency characteristic of the initial state.
Therefore, the frequency characteristic when the state before adjustment is multiplied by n is that only the gain diagram of the Bode plot changes, and 20log ₁₀ (n) shown in Formula 3 is the frequency characteristic when the state before adjustment is multiplied by n. and the frequency characteristic in the state before adjustment (C ₂ −C ₁ ).

The frequency characteristic prediction unit 403 reads the open-loop frequency characteristic Po of input/output gain and phase delay from the frequency characteristic storage unit 401, adds the difference (C 2 −C 1 ) to this open-loop frequency characteristic Po, and calculates the difference (C ₂ −C ₁ ). , find the estimated value Eo of the open-loop frequency characteristic. Then, the frequency characteristic prediction unit 403 calculates the estimated value Ec of the closed-loop frequency characteristic by using the estimated value Eo of the open-loop frequency characteristic, Ec=((C ₂ −C ₁ )+Po)(1+(C ₂ −C ₁ ) +Po).

The frequency characteristic prediction unit 403 operates in conjunction with the control parameter adjustment unit 404, which will be described later, to optimize control parameters for each adjustment condition, obtain an optimized open-loop frequency characteristic or a closed-loop frequency characteristic (estimated value), It is output to the presentation section 407 and the evaluation index calculation section 406.
Specifically, the frequency characteristic prediction unit 403 outputs the open-loop frequency characteristic or the closed-loop frequency characteristic (estimated value) corresponding to the optimized control parameter regarding “standard” to the presentation unit 407 and the evaluation index calculation unit 406. do.
In addition, the frequency characteristic prediction unit 403 displays the open-loop frequency characteristic or closed-loop frequency characteristic (estimated value) output from the frequency characteristic prediction unit 403, which corresponds to the optimized control parameter related to “emphasis on stability”, to the presentation unit 407. and output to the evaluation index calculation unit 406.
Further, the frequency characteristic prediction unit 403 outputs the open-loop frequency characteristic or the closed-loop frequency characteristic output from the frequency characteristic storage unit 401 using the initial control parameters before adjustment to the presentation unit 407 and the evaluation index calculation unit 406. .

By using the frequency characteristic prediction unit 403 described above, it is possible to calculate the estimated value Ec of the closed-loop frequency characteristic of the input/output gain and phase delay of the motor control unit 100 with the adjusted control parameters. This can be determined in a shorter time than when the motor control unit 100 is operated using parameters to actually detect the speed command and detected speed, and the frequency characteristic measuring unit 300 measures the closed-loop frequency characteristic.

(Control parameter adjustment unit 404)
The control parameter adjustment unit 404 acquires image data including a closed curve passing through a gain margin and a phase margin on a complex plane from the adjustment condition setting unit 402 .
In the following explanation, the control parameter adjustment unit 404 first obtains image data in which a circle C2 with a small diameter corresponding to "Standard" is drawn on a complex plane as a closed curve, and then selects "Stability Emphasis". A description will be given assuming that image data is obtained in which the corresponding circle C1 with a large diameter is drawn on a complex plane as a closed curve.
Further, the control parameter adjustment unit 404 receives the frequency characteristics (estimated value of the open-loop frequency characteristic or closed-loop frequency (estimated value of the characteristic).

The control parameter adjustment section 404 is created from the open-loop frequency characteristic H(jω)' (estimated value) or the frequency characteristic G(jω)' (estimated value) of the closed-loop frequency characteristic output from the frequency characteristic prediction section 403. A Nyquist locus is drawn on a complex plane including a circle C2, which is a closed curve, and a unit circle. A method for creating a Nyquist trajectory will be described later.

FIG. 6 is a diagram illustrating a Nyquist locus, a unit circle, and a circle passing through a gain margin and a phase margin drawn on a complex plane.
The control parameter adjustment unit 404 adjusts the integral gain K1v and proportional gain K2v of the speed control unit 120 and the coefficients ω _c , τ of the transfer function of the filter 130, which are control parameters, so that the Nyquist locus does not pass inside the circle C2. , δ is output to the frequency characteristic prediction unit 403.
By repeating adjustment of the control parameters between the frequency characteristic prediction unit 403 and the control parameter adjustment unit 404, the control parameters are optimized so that the Nyquist locus does not pass inside the circle C2. The control parameter adjustment unit 404 stores the optimized control parameters in the control parameter storage unit 405 as control parameters related to “standard”.

After determining the control parameters related to "Standard", the control parameter adjustment unit 404 obtains image data in which a circle C1 with a large diameter corresponding to "Stability Emphasis" is drawn as a closed curve on a complex plane. Similarly to the control parameters related to "Standard", control parameters related to "Stability Emphasis" are determined and stored in the control parameter storage unit 405.

The method by which the control parameter adjustment unit 404 creates the Nyquist trajectory will be specifically described below.
The frequency characteristic measuring unit 300 measures the closed-loop frequency characteristic measured by driving the motor control unit 100 using the initial control parameters (integral gain K1v, proportional gain K2v, and coefficients ω _c , τ, δ) before adjustment. and the open-loop frequency characteristic calculated from this closed-loop frequency characteristic are stored in the frequency characteristic storage unit 401.
The frequency characteristic measuring section 300 calculates the open loop frequency characteristic from the closed loop frequency characteristic as follows.
The velocity feedback loop consists of a subtractor 110 and an open loop circuit with a transfer function H. As already explained, the open loop circuit is composed of the speed control section 120, the filter 130, the current control section 140, and the motor 150 shown in FIG. When the input/output gain of the velocity feedback loop at a certain frequency ω ₀ is c, and the phase delay is θ, the closed loop frequency characteristic G(jω ₀ ) becomes c·e ^jθ . The closed-loop frequency characteristic G(jω ₀ ) is expressed as G(jω ₀ )=H(jω ₀ ₎ /(1+H(jω ₀ )) using the open-loop frequency characteristic H(jω 0 ). Therefore, the open loop frequency characteristic H(jω ₀ ) at a certain frequency ω ₀ is H(jω ₀ )=G(jω ₀ )/(1−G(jω ₀ ))=c・e ^jθ /(1−c・e ^jθ ).

The frequency characteristic prediction unit 403 obtains an open-loop frequency characteristic Po (Po=H(jω)) or a closed-loop frequency characteristic Pc (Pc=G(jω)) based on the initial control parameters before adjustment from the frequency characteristic storage unit 401. is read out, and using Equation 3, the estimated value Eo of the open-loop frequency characteristic or the estimated value Ec of the closed-loop frequency characteristic when using the adjusted control parameters is determined and output to the control parameter adjustment section 404.
The control parameter adjustment section 404 estimates the open-loop frequency characteristic Eo (Eo=H(jω)') or the closed-loop frequency characteristic estimate (Ec=G(jω)') obtained from the frequency characteristic measurement section 300. Create a Nyquist locus by drawing on the complex plane.

(Control parameter storage unit 405)
The control parameter storage unit 405 stores control parameters related to "standard" and control parameters related to "safety emphasis".

(Evaluation index calculation unit 406)
The evaluation index calculation unit 406 calculates the evaluation index related to “before adjustment” and outputs it to the presentation unit 407. The evaluation index related to "before adjustment" is, for example, the gain margin and phase margin calculated based on the open-loop frequency characteristics corresponding to the initial control parameters before adjustment, and the closed-loop frequency characteristics corresponding to the initial control parameters before adjustment. It is at least one of three control bands calculated based on frequency characteristics.
The evaluation index calculation unit 406 calculates the evaluation index regarding “standard” and outputs it to the presentation unit 407. The evaluation index related to "Standard" is based on the gain margin and phase margin calculated based on the open-loop frequency characteristic corresponding to the control parameter related to "Standard", and the closed-loop frequency characteristic corresponding to the control parameter related to "Standard", for example. is at least one of the three control bands calculated by
Furthermore, the evaluation index calculating unit 406 calculates an evaluation index related to “emphasis on stability” and outputs it to the presentation unit 407. The evaluation index related to "emphasis on stability" corresponds to, for example, the gain margin and phase margin calculated based on the open-loop frequency characteristic corresponding to the control parameter related to "emphasis on stability", and the control parameter related to "emphasis on stability". at least one of the three control bands calculated based on the closed-loop frequency characteristics.
Control band means the frequency where the gain intersects 0 dB or -3 dB.

(Presentation unit 407)
The presentation unit 407 receives from the frequency characteristic prediction unit 403 the open-loop frequency characteristic and/or closed-loop frequency characteristic corresponding to the initial control parameters before adjustment, and the open-loop frequency characteristic and/or closed-loop frequency characteristic corresponding to the control parameter related to “standard”. A frequency characteristic and an open-loop frequency characteristic and/or a closed-loop frequency characteristic corresponding to a control parameter regarding "stability emphasis" are obtained. The presentation unit 407 also acquires, from the evaluation index calculation unit 406, an evaluation index related to “before adjustment”, an evaluation index related to “standard”, and an evaluation index related to “stability emphasis”.
The presenting unit 407 then displays the open-loop frequency characteristic and/or closed-loop frequency characteristic corresponding to the initial control parameter before adjustment, the open-loop frequency characteristic and/or closed-loop frequency characteristic corresponding to the control parameter regarding "standard", and the " A Bode diagram is created from the open-loop frequency characteristics and/or closed-loop frequency characteristics corresponding to the control parameters related to "Stability Emphasis", and displayed on the screen together with evaluation indicators related to "Before Adjustment", "Standard", and "Stability Emphasis". to be displayed.
The presentation of the Bode diagram and the evaluation index by the presentation unit 407 is not limited to display on a display screen, but may also be presented on paper printed out by a printer or the like. In the following description, an example in which the presentation unit 407 displays on the display screen will be described.

FIG. 7 is a display screen displaying Bode diagrams of closed-loop frequency characteristics for "before adjustment", "standard", and "emphasis on stability" and evaluation indicators regarding "before adjustment", "standard", and "emphasis on stability". FIG. In FIG. 7, all of the gain margin, phase margin, and control band are displayed, but one or two of the gain margin, phase margin, and control band may be displayed.
In FIG. 7, in a table 501 displayed on a display screen 500, open-loop frequency characteristics and closed-loop frequency characteristics regarding "before adjustment", "standard", and "stability emphasis" are shown in

display columns

502A, 502B, and 502C, respectively. A Bode diagram is shown, and evaluation indicators regarding "before adjustment", "standard", and "stability emphasis" are shown in display columns 503A, 503B, and 503C, respectively.

8, 9, and 10 are Bode diagrams shown in

display columns

502A, 502B, and 502C, respectively. In FIGS. 8, 9, and 10, solid lines indicate closed-loop frequency characteristics, and broken lines indicate open-loop frequency characteristics.
As mentioned above, the gain margin and phase margin are calculated based on the open-loop frequency characteristics, and the control band is calculated based on the closed-loop frequency characteristics. Therefore, when displaying all of the gain margin, phase margin, and control band in the display columns 503A, 503B, and 503C, the

display columns

502A, 502B, and 502C should be opened as shown in FIGS. A Bode diagram showing loop frequency characteristics and closed loop frequency characteristics is shown.
When displaying only one or both of the gain margin and the phase margin in the display columns 503A, 503B, and 503C, Bode diagrams showing open-loop frequency characteristics are shown in the

display columns

502A, 502B, and 502C. When displaying only the control band in display columns 503A, 503B, and 503C, Bode diagrams showing closed-loop frequency characteristics are shown in

display columns

502A, 502B, and 502C.

The presentation unit 407 displays Bode diagrams of open-loop frequency characteristics and closed-loop frequency characteristics regarding "before adjustment", "standard", and "stability emphasis", and Bode diagrams regarding "before adjustment", "standard", and "stability emphasis". Either one of the evaluation indicators may be displayed.
The presentation unit 407 may show open-loop frequency characteristics and/or closed-loop frequency characteristics regarding a plurality of adjustment conditions in one Bode diagram. FIG. 11 is a Bode diagram in which the open-loop frequency characteristics and closed-loop frequency characteristics for "Standard" and "Stability Emphasis" are added to the open-loop frequency characteristics and closed-loop frequency characteristics for "Responsivity Emphasis" and "Custom". . In FIG. 11, it is of course possible to show open-loop frequency characteristics or closed-loop frequency characteristics regarding a plurality of adjustment conditions in one Bode diagram, if necessary.
In this way, by showing the open-loop frequency characteristics and/or closed-loop frequency characteristics of the four adjustment conditions of "Standard", "Stability Emphasis", "Responsivity Emphasis", and "Custom" in one Bode diagram, it is possible to It becomes easy to compare open-loop frequency characteristics and/or closed-loop frequency characteristics under two adjustment conditions.

Note that the presentation unit 407 does not need to display the Bode diagram of the open-loop frequency characteristic and/or the closed-loop frequency characteristic regarding “before adjustment” and the evaluation index regarding “before adjustment”. In this case, the evaluation index calculation unit 406 does not need to calculate the evaluation index regarding "before adjustment."

(Control parameter setting section 408)
The user can select the open-loop frequency characteristics and/or closed-loop frequency characteristics corresponding to the initial control parameters "before adjustment" and the open-loop frequency characteristics corresponding to the "standard" control parameters displayed on the display screen of the presentation unit 407. and/or looking at open-loop frequency characteristics and/or closed-loop frequency characteristics corresponding to control parameters regarding closed-loop frequency characteristics and “stability-oriented”, and evaluation indicators regarding “before adjustment”, “standard”, and “stability-oriented”. , determine the adjustment conditions.
The control parameter setting unit 408 displays a setting screen shown in FIG. 12, and the user selects the determined adjustment condition on the setting screen. The user selects and inputs standard from among the four adjustment conditions shown in FIG. 12, for example, standard, stability-oriented, responsiveness-oriented, and custom. Then, the control parameter setting unit 408 reads out the control parameters related to “standard” from the control parameter storage unit 405 and sets them as control parameters for the motor control unit 100.

Note that the user operates the motor control unit 100 set to the control parameters determined by the adjustment unit 400 of the present embodiment, and measures the frequency characteristics with the frequency characteristic measurement unit 300. The effectiveness of control parameters can be verified.

Next, the operation of the adjustment section 400 in this embodiment will be explained with reference to the flowchart in FIG. 13.

In step S11, the closed-loop frequency characteristic and open-loop frequency characteristic of input/output gain and phase delay output from the frequency characteristic measurement section 300 are stored in the frequency characteristic storage section 401.

In step S12, the adjustment condition setting unit 402 displays a setting screen for inputting adjustment conditions, and selects a plurality of open-loop circuits of the motor control unit 100 for each adjustment condition of the plurality of adjustment conditions input by the user. Set the stability margin (gain margin and phase margin) for

In step S13, the control parameter adjustment unit 404 outputs control parameter adjustment information to the frequency characteristic prediction unit 403, and the process moves to step S14.

In step S14, the frequency characteristic prediction unit 403 calculates the frequency characteristics C1 and C2 using the control parameters before and after adjustment, which have already been explained, and uses the frequency characteristics C1 and C2 and the open loop frequency acquired from the frequency characteristic storage unit 401. Based on the characteristic Po, the open-loop frequency characteristic after adjustment of the control parameters under one of the plurality of adjustment conditions is predicted. After predicting the open-loop frequency characteristics, the open-loop frequency characteristics are also used to predict the closed-loop frequency characteristics.

In step S15, the control parameter adjustment unit 404 determines whether stability conditions (stability margin, etc.) are satisfied.

In step S16, the control parameter adjustment unit 404 stores the adjusted and optimized control parameters in the control parameter storage unit 405.
In step S17, it is determined whether there are other adjustment conditions. If there are other adjustment conditions, the process returns to step S13, and if there are no other adjustment conditions, the process moves to step S18.
In step S18, the presentation unit 407 displays the frequency characteristics under a plurality of adjustment conditions, and also displays the evaluation index calculated by the evaluation index calculation unit 406.

According to the present embodiment described above, a plurality of frequency characteristics when control parameters such as motor gain and filter coefficients are adjusted under a plurality of adjustment conditions can be determined by one frequency characteristic measurement. As a result, by checking multiple frequency characteristics and/or evaluation indices of multiple frequency characteristics, you can easily compare the frequency characteristics and/or evaluation indices of frequency characteristics after adjustment under different adjustment conditions, and apply the control you want to apply. It becomes possible to easily select parameters.

<Modified example in which the control parameter adjustment unit is a machine learning unit>
FIG. 14 is a block diagram showing a modification example in which the control parameter adjustment section 404 of the adjustment section 400 shown in FIG. 3 is replaced with a machine learning section 600.
The adjustment unit 400A is the same as the adjustment unit 400 shown in FIG. 3 except that the machine learning unit 600 is used as the control parameter adjustment unit 404.
In the following explanation, a case will be described in which the machine learning unit 600 performs reinforcement learning, but the learning performed by the machine learning unit 600 is not particularly limited to reinforcement learning, and the present invention is also applicable to cases where supervised learning is performed, for example. It is.

The machine learning unit 600 sets the estimated values of the input/output gain and phase delay output from the frequency characteristic prediction unit 403 as a state S, and sets the adjustment of the control parameter value related to the state S as an action A.Q Perform learning (Q-learning). As is well known to those skilled in the art, the purpose of Q learning is to select the action A with the highest value Q(S, A) as the optimal action from among possible actions A in a certain state S. do.

Specifically, an agent (machine learning device) selects various actions A under a certain state S, and selects a better action based on the reward given for the action A at that time. By doing so, the correct value Q(S,A) is learned.

Furthermore, since we want to maximize the total amount of rewards that can be obtained in the future, we aim to finally achieve Q(S,A)=E[Σ(γ ^t )r _t ]. Here, E[ ] represents the expected value, t is time, γ is a parameter called a discount rate which will be described later, r _t is the reward at time t, and Σ is the sum at time t. The expected value in this equation is the expected value when the state changes according to the optimal action. Such an update formula for the value Q(S, A) can be expressed, for example, by the following Equation 6 (shown as Equation 6 below).

In Equation 6 above, S _t represents the state of the environment at time t, and A _t represents the behavior at time t. Due to the action A _t , the state changes to S _t+1 . r _t+1 represents the reward obtained by changing the state. Moreover, the term with max is the Q value when action A with the highest Q value known at that time is selected under state S _t+1 multiplied by γ. Here, γ is a parameter satisfying 0<γ≦1 and is called a discount rate. Further, α is a learning coefficient and is in the range of 0<α≦1.

Equation 6 above represents a method of updating the value Q(S _t , _{A t} ₎ of action A t in state S _t based on the reward r _t+1 returned as a result of trial A _t .

The machine learning unit 600 determines the action A by observing the state information S including the frequency characteristics of the input/output gain and phase delay for each frequency estimated by the frequency characteristic prediction unit 403. The machine learning unit 600 receives a reward each time it performs action A. The remuneration will be discussed later.
In Q-learning, the machine learning unit 600 searches, for example, by trial and error for the optimal action A that maximizes the total reward over the future. By doing so, the machine learning unit 600 can select the optimal action A (that is, the optimal servo parameter value) for the state S.

FIG. 15 is a block diagram showing the configuration of the machine learning section 600.
In order to perform the reinforcement learning described above, as shown in FIG. 15, the machine learning unit 600 includes a state information acquisition unit 601, a learning unit 602, a behavior information output unit 603, a value function storage unit 604, and an optimization behavior information output unit. 605. The learning unit 602 includes a reward output unit 6021, a value function update unit 6022, and a behavior information generation unit 6023.

The state information acquisition unit 601 acquires from the frequency characteristic prediction unit 403 the estimated value of the frequency characteristic of the input/output gain and phase delay of the motor control unit 100 calculated using the adjusted control parameters, and outputs it to the learning unit 602. do. This state information S corresponds to the environmental state S in Q learning. Further, the state information acquisition unit 601 acquires image data regarding a complex plane including a circle that is a closed curve and a unit circle from the adjustment condition setting unit 402 and outputs it to the learning unit 602.

Note that the integral gain K1v and proportional gain K2v of the speed control unit 120 at the time when Q learning is first started, and the coefficients ω _c , τ, and δ of the transfer function of the filter 130 are generated by the user in advance. . In this embodiment, the initial setting values of the integral gain K1v and proportional gain K2v of the speed control unit 120 and/or the coefficients ω _c , τ, and δ of the transfer function of the filter 130, created by the user, are optimized by reinforcement learning. adjust to something.
Note that if the operator has adjusted the machine tool in advance, the integral gain K1v, the proportional gain K2v, and the coefficients ω _c , τ, and δ may be machine learned using the adjusted values as initial values.

The learning unit 602 is a part that learns the value Q(S, A) when selecting a certain action A under a certain environmental state S.

First, the reward output unit 6021 of the learning unit 602 will be explained.
The reward output unit 6021 is a part that obtains a reward when action A is selected under a certain state S.

The reward output unit 6021 acquires image data regarding a complex plane including a circle that is a closed curve and a unit circle from the state information acquisition unit 601. The reward output unit 6021 uses the input/output gain and phase delay obtained from the state information acquisition unit 601 to create a Nyquist trajectory by drawing the open-loop frequency characteristic H(jω) on the acquired complex plane. The method for creating the Nyquist trajectory has already been explained in the operation description of the adjustment section 400, so it will not be described here. In this way, a complex plane showing a circle passing through the Nyquist locus, the unit circle, and the gain margin and phase margin shown in FIG. 5 is obtained.
The Nyquist locus in the initial state before adjustment of the control parameters is determined by the reward output unit 6021, which acquires the open-loop frequency characteristic H(jω) from the frequency characteristic storage unit 401 via the state information acquisition unit 601, and converts it into an open-loop frequency characteristic. It can be created by drawing H(jω) on a complex plane.
The Nyquist trajectory in the process of Q learning is created by the reward output unit 6021 drawing the open-loop frequency characteristic H(jω)' or the closed-loop frequency characteristic G(jω)' output from the frequency characteristic prediction unit 403 on a complex plane. Can be created.

In the following explanation, the radius of the circle is assumed to be the radius r, and the shortest distance between the circle and the Nyquist locus is assumed to be the shortest distance d. Here, the shortest distance d is the shortest distance between the center of the circle and the Nyquist locus, but is not limited to this, and may be, for example, the shortest distance between the outer circumference of the circle and the Nyquist locus.

The reward output unit 6021 gives a negative reward when the shortest distance d is smaller than the radius r (d<r) and the Nyquist trajectory passes inside the closed curve. On the other hand, the reward output unit 6021 gives a reward of zero value when the shortest distance d is equal to or larger than the radius r (d≧r) and the Nyquist trajectory does not pass inside the circle.

By giving the reward as described above, the reward output unit 6021 sets the integral gain K1v of the speed control unit 120 such that the Nyquist locus does not pass inside the circle and the gain margin and phase margin are equal to or higher than the values set by the user. The proportional gain K2v and the coefficients ω _c , τ, and δ of the transfer function of the filter 130 are searched by trial and error.

In the example explained above, whether the Nyquist locus passes inside the circle that is a closed curve is determined based on the shortest distance between the circle and the Nyquist locus, but the method is not limited to this, and other methods may also be used. For example, the determination may be made based on whether the Nyquist locus touches or intersects with the outer circumference of a circle that is a closed curve.

(Example considering response speed)
When the Nyquist trajectory passes on a circle (d=r) or outside the circle (d>r), the further the Nyquist trajectory is from the circle, the larger the gain margin and phase margin become, increasing the stability of the servo system, but the feedback The gain decreases and the response speed decreases.
Therefore, it is desirable that the reward output unit 6021 gives the reward so that the feedback gain is as large as possible, exceeding the gain margin and phase margin determined by the user. Three examples of how the reward output unit 6021 determines the reward so as to make the feedback gain as large as possible beyond the gain margin and phase margin determined by the user will be described below.

(1) Method for determining remuneration based on cut-off frequency The remuneration output unit 6021 outputs the input/output gain and phase delay of the motor control unit 100 calculated using the adjusted control parameters output from the frequency characteristic prediction unit 403. Create a Bode diagram from and find the cutoff frequency.
The cutoff frequency is, for example, a frequency at which the gain characteristic of the Bode diagram becomes -3 dB or a frequency at which the phase characteristic becomes -180 degrees.

The reward output unit 6021 determines the reward so that the cutoff frequency becomes large.
Specifically, the reward output unit 6021 corrects the integral gain K1v, the proportional gain K2v, and/or the coefficients ω _c , τ, and δ, and changes the cutoff frequency when the state S before the correction changes to the state S′. The reward is determined depending on whether fcut becomes larger, the same, or smaller. In the following description, the cutoff frequency fcut in state S is written as fcut(S), and the cutoff frequency fcut in state S' is written as fcut(S').

When the state changes from state S to state S' and the cutoff frequency fcut becomes large, the reward output unit 6021 outputs a positive value as cutoff frequency fcut(S')>cutoff frequency fcut(S). Reward.
When the state changes from state S to state S' and the cutoff frequency fcut does not change, the reward output unit 6021 outputs a reward of zero value as cutoff frequency fcut(S')=cutoff frequency fcut(S). give.
When the state changes from state S to state S' and the cutoff frequency fcut becomes smaller, the reward output unit 6021 outputs a negative value as cutoff frequency fcut(S')<cutoff frequency fcut(S). Reward.

By determining the reward as described above, the integral gain K1v and proportional gain K2v of the speed control unit 120 and/or filter The coefficients ω _c , τ, and δ of the 130 transfer functions are searched by trial and error.
By increasing the cutoff frequency fcut, the feedback gain increases and the response speed becomes faster.

(2) Method for determining remuneration based on closed-loop characteristics The remuneration output unit 6021 calculates the input/output gain and phase delay of the motor control unit 100 using the adjusted control parameters output from the frequency characteristic prediction unit 403. , find the closed-loop transfer function G(jω). The reward output unit 6021 can apply f=Σ|1−G(jω)| ² as the evaluation function f in a preset frequency domain.
The reward output unit 6021 determines the reward so that the value of the evaluation function f becomes small.
Specifically, the reward output unit 6021 corrects the integral gain K1v, the proportional gain K2v, and/or the coefficients ω _c , τ, and δ, and when the state S before the correction changes to the state S′, the evaluation function The reward is determined depending on whether the value of f becomes smaller, the same, or larger. In the following description, the value of the evaluation function f in the state S is written as f(S), and the value of the evaluation function f in the state S' is written as f(S').
As the value of the evaluation function f becomes smaller, the cut frequency of the closed-loop Bode diagram shown in FIG. 11 becomes larger.

When the value of the evaluation function f becomes smaller when the state changes from the state S to the state S', the reward output unit 6021 determines that the value of the evaluation function f(S')<the value of the evaluation function f(S), Give a reward of value.
If the value of the evaluation function f does not change when the state changes from the state S to the state S', the reward output unit 6021 sets the evaluation function value f(S')=the evaluation function value f(S) to be zero. Give value rewards.
When the value of the evaluation function f becomes large when the state changes from the state S to the state S', the reward output unit 6021 outputs a negative value as the evaluation function value f(S')>the evaluation function value f(S). Give a reward of value.

By determining the reward as described above, the integral gain K1v and proportional gain K2v of the speed control unit 120 and the filter 130 are set so that the value of the evaluation function f becomes small when the Nyquist trajectory passes on a circle or outside the circle. The coefficients ω _c , τ, and δ of the transfer function are searched by trial and error.
As the value of the evaluation function f becomes smaller, the feedback gain increases and the response speed becomes faster.

(3) A method to determine the reward so that the shortest distance d approaches the radius r When the Nyquist trajectory passes on a circle (d=r) or outside the circle (d>r), the Nyquist trajectory approaches a closed curve. Decide on compensation.
Specifically, the reward output unit 6021 corrects the integral gain K1v, the proportional gain K2v, and/or the coefficients ω _c , τ, and δ, and when the state S before the correction changes to the state S′, the The reward is determined depending on whether the shortest distance d between the center and the Nyquist trajectory becomes smaller, the same, or larger. In the following description, the shortest distance d in state S is written as d(s), and the shortest distance d in state S' is written as d(s').

When the state changes from state S to state S', when the shortest distance d becomes smaller, the reward output unit 6021 gives a positive value reward as shortest distance d(S')<shortest distance d(S). .
When the state changes from the state S to the state S', if the shortest distance d does not change, the reward output unit 6021 gives a reward of zero as the shortest distance d(S')=the shortest distance d(S).
When the state changes from state S to state S', when the shortest distance d becomes larger, the reward output unit 6021 gives a negative reward as shortest distance d(S')>shortest distance d(S). .

By determining the reward as described above, the integral gain K1v and proportional gain K2v of the speed control unit 120 and/or the coefficient ω _c of the transfer function of the filter 130 are adjusted such that the Nyquist locus passes on a circle or approaches the outer periphery of the circle. , τ, and δ are searched by trial and error.
When the Nyquist locus passes on a circle or approaches the outer periphery of the circle, the feedback gain increases and the response speed becomes faster.
The method of determining the reward based on the information on the shortest distance d is not limited to the above method, and other methods can be applied.

(Example considering resonance)
Even when the Nyquist locus passes on a circle (d=r) or outside the circle (d>r), the input/output gain may increase due to resonance at the machine end of the machine to be controlled.
Therefore, it is desirable that the reward output unit 6021 determines the reward so as to suppress resonance by having a gain margin and a phase margin determined by the user. Below, we will explain how to determine rewards by comparing open-loop characteristics and a normative model.

Hereinafter, an operation in which the reward output unit 6021 gives a negative reward when the input/output gain for each frequency in the created frequency characteristic is larger than the input/output gain of the reference model will be described using FIGS. 16 and 17.

The reward output unit 6021 stores a reference model of input/output gain. The reference model is a model of a motor control unit that has ideal characteristics without resonance. The reference model can be calculated from the inertia Ja, torque constant K _t , proportional gain K _p , integral gain K _I , and differential gain K _D of the model shown in FIG. 16, for example. Inertia Ja is the sum of motor inertia and mechanical inertia.

FIG. 17 is a characteristic diagram showing the frequency characteristics of the input/output gain of the motor control section of the reference model and the motor control section 100 before and after learning. As shown in the characteristic diagram of FIG. 17, the reference model has a frequency region FA that is an ideal input/output gain above a certain input/output gain, for example, -20 dB or above, and a frequency region below a certain input/output gain. It has a region FB which is a frequency region. In the region FA of FIG. 17, the ideal input/output gain of the reference model is shown by a curve MC ₁ (thick line). In the region FB of FIG. 17, the ideal virtual input/output gain of the reference model is shown by a curve MC ₁₁ (thick broken line), and the input/output gain of the reference model is shown as a constant value by a straight line MC ₁₂ (thick line). In regions FA and FB in FIG. 13, input/output gain curves with the motor control unit before and after learning are shown by curves RC ₁ and RC ₂ , respectively.

In the area FA, the reward output unit 6021 outputs a negative reward if the pre-learning curve _RC1 of the input/output gain for each frequency in the created frequency characteristic exceeds the ideal input/output gain curve _MC1 of the reference model. give.
In the region FB exceeding the frequency where the input/output gain is sufficiently small, even if the input/output gain curve _RC1 before learning exceeds the ideal virtual input/output gain curve _MC11 of the reference model, there is no effect on stability. becomes smaller. Therefore, in the region FB, as described above, the input/output gain of the reference model is not the ideal gain characteristic curve _MC11 , but a straight line _MC12 with a constant value of input/output gain (for example, −20 dB). However, if the input/output gain curve _RC1 measured before learning exceeds the input/output gain straight line _MC12 having a constant value, it may become unstable, and therefore a negative value is given as a reward.

Note that when adjusting the input/output gain, the integral gain K1v and proportional gain K2v of the speed control section 120 and/or the coefficients ω _c , τ, and δ of the transfer function of the filter 130 are adjusted. As for the characteristics of the filter 130, the gain and phase change depending on the bandwidth fw of the filter 130, and the gain and phase change depending on the attenuation coefficient k of the filter 130. Therefore, by adjusting the coefficients of the filter 130, the input/output gain can be adjusted.

If the shortest distance d is smaller than the radius r (d<r) and the Nyquist locus passes inside a closed curve, and a negative value reward is given, the reward output unit 6021 converts this negative value reward into a value function. It is output to the update unit 6022. If the shortest distance d is equal to or larger than the radius r (d≧r) and the Nyquist trajectory does not pass inside the circle, and a positive value reward is given, the reward output unit 6021 outputs the positive value. The reward is output to the value function update unit 6022.
When the reward is given in the three examples that take response speed into consideration or the example that takes resonance into account, the reward output unit 6021 adds a positive value reward that is given when the Nyquist trajectory does not pass inside the circle to this reward. The total reward obtained by adding the above is output to the value function updating unit 6022.

Note that when adding rewards, weights may be given to the rewards. For example, if the stability of the servo system is important, the positive reward given when the Nyquist trajectory does not pass inside the circle is the reward given in three examples that take response speed into consideration or the example that takes resonance into account. It is possible to give a weight that makes it more important than the other item.
The reward output unit 6021 has been described above.

The value function updating unit 6022 performs Q learning based on the state S, the action A, the state S′ when the action A is applied to the state S, and the reward obtained as described above. The value function Q stored in the value function storage unit 604 is updated.
The value function Q may be updated by online learning, batch learning, or mini-batch learning.
Online learning is a learning method in which, by applying a certain action A to the current state S, the value function Q is immediately updated each time the state S transitions to a new state S'. In addition, batch learning collects learning data by applying a certain action A to the current state S, repeating the transition from state S to a new state S', and This is a learning method that updates the value function Q using learning data. Furthermore, mini-batch learning is an intermediate learning method between online learning and batch learning, in which the value function Q is updated every time a certain amount of learning data is accumulated.

The behavior information generation unit 6023 selects behavior A in the Q learning process for the current state S. In the process of _Q learning, the behavior information generation unit 6023 performs an operation (Q learning In order to cause the user to perform the action A (corresponding to action A in ), action information A is generated and the generated action information A is output to the action information output unit 603 .
More specifically, the behavior information generation unit 6023 generates, for example, the integral gain K1v and proportional gain K2v of the speed control unit 120 and/or the coefficients ω _c , τ of the transfer function of the filter 130, which are included in the state S. The integral gain K1v and proportional gain K2v of the speed control unit 120 and the coefficients ω _c , τ, and δ of the transfer function of the filter 130, which are included in the action A, may be incrementally added to or subtracted from δ.

Note that all of the integral gain K1v and proportional gain K2v of the speed control unit 120 and the coefficients ω _c , τ, and δ of the filter 130 may be modified, or some of the coefficients may be modified. When modifying the coefficients ω _c , τ, and δ of the filter 130, for example, the center frequency fc that causes resonance is easy to find, and the center frequency fc is easy to specify. Therefore, the behavior information generation unit 6023 temporarily fixes the center frequency fc, modifies the bandwidth fw and the attenuation coefficient δ, that is, fixes the coefficient ω _c (=2πfc), and fixes the coefficient τ (=fw/fc). In order to perform an operation of correcting the damping coefficient δ, the behavior information A may be generated and the generated behavior information A may be output to the behavior information output unit 603.

In addition, the behavior information generation unit 6023 uses the greedy method to select the behavior A′ with the highest value Q(S,A) among the currently estimated values of the behavior A, or randomly performs a behavior with a certain small probability ε. A' may be selected by a known method such as the ε greedy method, in which the action A' with the highest value Q(S, A) is selected otherwise.

The behavior information output unit 603 is a part that transmits the behavior information A output from the learning unit 602 to the frequency characteristic prediction unit 403. As described above, the filter 130 determines the current state S, that is, the currently set integral gain K1v and proportional gain K2v of the speed control unit 120 and/or each coefficient ω _c , τ, based on this behavior information. By slightly modifying δ, a transition is made to the next state S' (that is, the modified integral gain K1v and proportional gain K2v of the speed control unit 120 and/or each coefficient of the filter 130).

The value function storage unit 604 is a storage device that stores the value function Q. The value function Q may be stored as a table (hereinafter referred to as an action value table) for each state S and action A, for example. The value function Q stored in the value function storage unit 604 is updated by the value function update unit 6022. Further, the value function Q stored in the value function storage unit 604 may be shared with other machine learning units 600. By sharing the value function Q among multiple machine learning units 600, it becomes possible to perform reinforcement learning in a distributed manner in each machine learning unit 600, which makes it possible to improve the efficiency of reinforcement learning. Become.

The optimization behavior information output unit 605 controls the speed control unit 120 and the filter 130 to perform an operation that maximizes the value Q(S, A) based on the value function Q updated by the value function update unit 6022 performing Q learning. Behavior information A (hereinafter referred to as "optimized behavior information") for causing the behavior to occur is generated.
More specifically, the optimization behavior information output unit 605 acquires the value function Q stored in the value function storage unit 604. This value function Q is updated by the value function updating unit 6022 by performing Q learning as described above. Then, the optimized behavior information output unit 605 generates behavior information based on the value function Q, and outputs the generated behavior information to the control parameter storage unit 405. This optimization behavior information includes the integral gain K1v and proportional gain K2v of the speed control unit 120, and/or each of the transfer function of the filter 130, as well as the behavior information output by the behavior information output unit 603 in the process of Q learning. Information for modifying the coefficients ω _c , τ, and δ is included.
Through the above operations, the machine learning unit 600 optimizes the integral gain K1v and the proportional gain K2v of the speed control unit 120 and/or the coefficients ω _c , τ, and δ of the transfer function of the filter 130, and can be operated so that the stability margin of is equal to or greater than a predetermined value.
In addition, with the above operation, the integral gain K1v and proportional gain K2v of the speed control section 120 and/or the coefficients ω _c , τ, and δ of the transfer function of the filter 130 are optimized, and the stability margin of the motor control section 100 is improved. It is possible to increase the feedback gain to a predetermined value or more and to increase the response speed and/or to suppress resonance.
As described above, by using the machine learning unit 600 of the present disclosure, it is possible to simplify the gain of the speed control unit 120 and the parameter adjustment of the filter 130.

(Second embodiment)
FIG. 18 is a block diagram illustrating a configuration example of the adjustment section included in the control system according to the second embodiment of the present disclosure.
The adjustment unit 400B shown in FIG. 18 differs from the adjustment unit 400 shown in FIG. 3 in that a time response prediction unit 409 and an evaluation index calculation unit 410 are provided.
The time response prediction unit 409, evaluation index calculation unit 410, presentation unit 407, and control parameter setting unit 408 will be described below.

(Time response prediction unit 409)
The time response prediction unit 409 acquires the open-loop frequency characteristic/or the closed-loop frequency characteristic in the initial state before adjustment from the frequency characteristic prediction unit 403, and predicts the time response before adjustment (which becomes the second time response). do. Further, the time response prediction unit 409 acquires the open-loop frequency characteristic/or the closed-loop frequency characteristic corresponding to the optimized control parameter regarding the “standard”, and calculates the time response (first predict the time response of In addition, the time response prediction unit 409 acquires the open-loop frequency characteristic/or the closed-loop frequency characteristic corresponding to the optimized control parameter related to "emphasis on stability", and calculates the time response corresponding to the adjustment condition of "emphasis on stability". Predict the response (which will be the first time response).
A method of predicting a time response using frequency characteristics involves performing modal analysis using information on frequency characteristics to create a transfer function model P(s). When this transfer function model P(s) is subjected to inverse Laplace transform, a time domain model y(t) is obtained. A method of predicting a time response using frequency characteristics is described in, for example, Japanese Patent No. 6,515,844.

An example of a method for predicting a time response using frequency characteristics will be described below.
The time response prediction unit 409 acquires the open-loop frequency characteristic/or the closed-loop frequency characteristic corresponding to the optimized control parameters related to "standard" and performs a modal analysis. Modal analysis means estimating the modal frequency ω and modal damping ratio ζ of mechanical vibration from frequency characteristics.
For example, a model P(s) of the transfer function of Equation 7 (Equation 7 below) is created by modal analysis. The first term on the right side of Equation 7 is a rigid body mode, and the second term is a resonance mode. ω _n and ζ _n represent the frequency and damping ratio of the n-th mode. K ₀ and K _n are coefficients.

Next, a principal component analysis is performed to obtain a transfer function model P(s) of Equation 8 (Equation 8 below). Principal component analysis is the process of extracting only the main (dominant) mode from among the multiple modes obtained from mode analysis.

Equations 7 and 8 above serve as a machine model when only the rigid mode and the first resonance mode are considered. Therefore, the characteristics of the machine can be expressed by a model with the minimum necessary degrees of freedom (modes).
FIG. 19 is a Bode diagram showing the first resonance mode and the second resonance mode, and Equations 7 and 8 are examples taking the first resonance mode into consideration.
Furthermore, by inverse Laplace transform of Equation 8 above, a time domain model y(t) is obtained.

(Evaluation index calculation unit 410)
The evaluation index calculation unit 410 calculates at least one evaluation index (evaluation index related to "before adjustment") among rise time, overshoot amount, settling time, etc., based on the time response corresponding to "before adjustment". and outputs it to the presentation section 407. In addition, the evaluation index calculation unit 410 calculates at least one evaluation index (evaluation index related to "standard") among rise time, overshoot amount, settling time, etc., based on the time response corresponding to the "standard" adjustment condition. is calculated and output to the presentation unit 407. Furthermore, the evaluation index calculation unit 410 calculates at least one evaluation index of rise time, overshoot amount, settling time, etc. ("stability") based on the time response corresponding to the "stability" adjustment condition. evaluation index) is calculated and output to the presentation unit 407.

(Presentation unit 407)
The presentation unit 407 displays a display screen shown in FIG. 20, which will be described below, in addition to the display screen shown in FIG.
The presentation unit 407 acquires from the time response prediction unit 409 the time response “before adjustment”, the time response corresponding to the “standard” adjustment condition, and the time response corresponding to the “stability-oriented” adjustment condition. In addition, the presentation unit 407 receives from the evaluation index calculation unit 406 the evaluation index related to the “before adjustment” time response, the evaluation index related to the time response corresponding to the “standard” adjustment condition, and the adjustment condition “emphasis on stability”. Obtain evaluation metrics regarding time response. The time response is, for example, a step response or an impulse response.
Then, the presentation unit 407 creates time response characteristic diagrams from the time response before adjustment, the time response corresponding to the "standard" adjustment condition, and the time response corresponding to the "stability-oriented" adjustment condition, respectively, It is displayed on the display screen 700 together with the evaluation index regarding the time response “before adjustment”, the evaluation index regarding the time response corresponding to the “standard” adjustment condition, and the evaluation index regarding the time response corresponding to the “stability emphasis” adjustment condition.
The presentation unit 407 displays characteristic diagrams of time responses corresponding to "before adjustment", "standard", and "emphasis on stability", and evaluation indicators regarding "before adjustment", "standard", and "emphasis on stability". Either one may be displayed.

FIG. 20 shows characteristic diagrams of time responses corresponding to “before adjustment,” “standard,” and “stability emphasis,” and evaluation indicators regarding time responses corresponding to “before adjustment,” “standard,” and “stability emphasis.” It is a figure which shows the display screen which displayed . In FIG. 20, all of the rise time, overshoot amount, and settling time are displayed, but one or two of the rise time, overshoot amount, and settling time may be displayed.
In FIG. 20, in a table 701 displayed on a display screen 700, time response characteristic diagrams corresponding to "before adjustment", "standard", and "stability emphasis" are shown in

display columns

702A, 702B, and 702C, respectively. In

display columns

703A, 703B, and 703C, evaluation indicators related to time response corresponding to "before adjustment,""standard," and "stability emphasis" are shown, respectively.
FIG. 21, FIG. 22, and FIG. 23 are diagrams showing characteristics of time responses shown in

display columns

702A, 702B, and 702C, respectively.
The presentation unit 407 may show time responses regarding a plurality of adjustment conditions in one characteristic diagram.
The presentation unit 407 may display the display screen shown in FIG. 20 alone or together with the display screen shown in FIG. 7. Furthermore, the presentation section 407 may include a presentation section that displays the display screen shown in FIG. 20 and a presentation section that displays the display screen shown in FIG. 7.
In this embodiment, when displaying only the display screen shown in FIG. 20 without displaying the display screen shown in FIG. It is not necessary to input the closed-loop frequency characteristic and/or the open-loop frequency characteristic.

Note that the presentation unit 407 does not need to display the evaluation index for the time response related to "before adjustment" and the time response related to "before adjustment." In this case, the evaluation index calculation unit 410 does not need to calculate the evaluation index for the time response related to "before adjustment."

(Control parameter setting section 408)
The user can view the time response characteristic diagram before adjustment, the time response characteristic diagram corresponding to the "standard" adjustment condition, and the time response characteristic diagram corresponding to the "stability emphasis" adjustment condition displayed on the display screen of the presentation unit 407. The adjustment conditions are determined by looking at the diagram and the evaluation index regarding the time response corresponding to "before adjustment,""standard," and "stability emphasis."
The control parameter setting unit 408 displays a setting screen shown in FIG. 12, and the user views this setting screen and selects adjustment conditions. For example, the user selects and inputs "standard" from among the four adjustment conditions: standard, stability-oriented, responsiveness-oriented, and custom. Then, the control parameter setting unit 408 reads out the control parameters related to “standard” from the control parameter storage unit 405 and sets them as control parameters for the motor control unit 100.
When the presentation unit 407 displays the display screen shown in FIG. 7 and the display screen shown in FIG. 20 together, the user can view both display screens and select adjustment conditions.

The functional blocks included in the control system 10 and the

adjustment units

400, 400A, and 400B have been described above.
In order to realize these functional blocks, the control system 10 or any of the

adjustment units

400, 400A, and 400B includes an arithmetic processing device such as a CPU (Central Processing Unit). In addition, each of the control system 10 or the

adjustment units

400, 400A, and 400B includes an auxiliary storage device such as an HDD (Hard Disk Drive) that stores various control programs such as application software or an OS (Operating System); It also includes a main storage device such as a RAM (Random Access Memory) for storing data temporarily required when the arithmetic processing unit executes a program.

Then, in either the control system 10 or the

adjustment units

400, 400A, and 400B, the arithmetic processing unit reads the application software or OS from the auxiliary storage device, and deploys the loaded application software or OS in the main storage device. Arithmetic processing is performed based on the application software or OS. Also, based on this calculation result, various hardware included in each device is controlled. Thereby, the functional blocks of this embodiment are realized. In other words, this embodiment can be realized through cooperation between hardware and software.

Since the machine learning unit 600 requires a large amount of calculations associated with machine learning, for example, a personal computer may be equipped with a GPU (Graphics Processing Units), and the GPU may be machined using a technology called GPGPU (General-Purpose computing on Graphics Processing Units). It is a good idea to use it for arithmetic processing associated with learning, as this will enable high-speed processing. Furthermore, in order to perform faster processing, multiple computers equipped with such GPUs are used to construct a computer cluster, and the multiple computers included in this computer cluster perform parallel processing. It's okay.

Each component included in the control system 10 and

adjustment units

400, 400A, and 400B described above can be realized by hardware, software, or a combination thereof. Further, the control parameter adjustment method performed by the cooperation of each component included in the control system 10 and the

adjustment units

400, 400A, and 400B can also be realized by hardware, software, or a combination thereof. . Here, being realized by software means being realized by a computer reading and executing a program.

The program can be stored and delivered to a computer using various types of non-transitory computer readable media. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (e.g., hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R/ W, semiconductor memory (eg, mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (random access memory)). The program may also be supplied to the computer via various types of transitory computer readable media.

Although the embodiments described above are preferred embodiments of the present invention, the scope of the present invention is not limited to only the above embodiments, and various modifications may be made without departing from the gist of the present invention. It is possible to implement

In the above-described embodiment, a case has been described in which one filter is provided, but the filter 130 may be configured by connecting a plurality of filters in series, each corresponding to a different frequency band. FIG. 24 is a block diagram showing an example of a filter configured by directly connecting a plurality of filters. In FIG. 24, when there are m resonance points (m is a natural number of 2 or more), the filter 130 is configured by connecting m filters 130-1 to 130-m in series. Optimum values are determined by machine learning for the coefficients ω _c , τ, and δ of each of the m filters 130-1 to 130-m.

In addition to the configuration shown in FIG. 1, the control system has the following configurations.
<Modified example in which the adjustment unit is provided outside the motor control unit via a network>
FIG. 25 is a block diagram showing another configuration example of the control system. The control system 10A shown in FIG. 25 is different from the control system 10 shown in FIG. It is connected to n adjustment sections 400-1 to 400-n, and each is provided with a frequency generation section 200 and a frequency characteristic measurement section 300.
Adjusting sections 400-1 to 400-n have the same configuration as adjusting

section

400, 400A, or 400B. Motor control units 100-1 to 100-n each correspond to a motor control device, and adjustment units 400-1 to 400-n each correspond to an adjustment device. Note that, of course, one or both of the frequency generation section 200 and the frequency characteristic measurement section 300 may be provided outside the motor control sections 100-1 to 100-n.

Here, the motor control section 100-1 and the adjustment section 400-1 are connected as a one-to-one pair so that they can communicate. Motor control units 100-2 to 100-n and adjustment units 400-2 to 400-n are also connected in the same way as motor control unit 100-1 and adjustment unit 400-1. In FIG. 25, n sets of motor control units 100-1 to 100-n and adjustment units 400-1 to 400-n are connected via a network 800. 100-n and adjustment units 400-1 to 400-n, the motor control unit and adjustment unit of each set may be directly connected via a connection interface. For example, the n sets of motor control units 100-1 to 100-n and adjustment units 400-1 to 400-n may be installed in the same factory, or may be installed in different factories. Good too.

Note that the network 800 is, for example, a LAN (Local Area Network) built within a factory, the Internet, a public telephone network, or a combination thereof. There are no particular limitations on the specific communication method in the network 800 or whether it is a wired connection or a wireless connection.

<Freedom of system configuration>
In the embodiment described above, the motor control units 100-1 to 100-n and the adjustment units 400-1 to 400-n are connected to each other in a one-to-one relationship for communication. The adjustment unit may be communicably connected to a plurality of motor control units via the network 800.
In this case, a distributed processing system may be used in which each function of one adjustment unit is distributed to a plurality of servers as appropriate. Furthermore, each function of one adjustment unit may be realized by using a virtual server function or the like on the cloud.

In addition, if there are n motor control units 100-1 to 100-n of the same model name, same specification, or same series, and n adjustment units 400-1 to 400-n corresponding to each adjustment unit, each adjustment The configuration may be such that the adjustment results among the units 400-1 to 400-n are shared. By doing so, it becomes possible to construct a more optimal model.

The adjustment device, control system, and control parameter adjustment method for adjusting control parameters according to the present disclosure can take various embodiments having the following configurations, including the embodiments described above.
(1) An adjustment device (for example,

adjustment unit

400, 400A) that adjusts control parameters of a motor control unit that controls a motor,
a frequency characteristic storage unit (for example, frequency characteristic storage unit 401) that stores the frequency characteristics of the machine measured by operating the motor control unit having control parameters before adjustment;
an adjustment condition setting section (for example, adjustment condition setting section 402) that sets a plurality of adjustment conditions for adjusting the control parameters of the motor control section;
A frequency characteristic prediction unit (e.g., frequency characteristic prediction unit 403);
Adjusting the control parameters input to the frequency characteristic prediction unit in order to optimize the control parameters using the predicted frequency characteristics and one of the plurality of adjustment conditions set by the adjustment condition setting unit. a control parameter adjustment unit (for example, control parameter adjustment unit 404),
a control parameter storage unit (for example, control parameter storage unit 405) that stores the plurality of control parameters optimized for the plurality of adjustment conditions;
an evaluation index calculation unit (for example, evaluation index calculation unit 406) that calculates an evaluation index of the frequency characteristic from the predicted frequency characteristic corresponding to the optimized control parameter;
a presentation unit (for example, presentation unit 407) that presents at least one of the predicted frequency characteristics and the evaluation index corresponding to the optimized control parameters for each adjustment condition of the plurality of adjustment conditions;
a control parameter setting section (for example, control parameter setting section 408) that sets a control parameter selected from the plurality of control parameters stored in the control parameter storage section in the motor control section;
Adjustment device with.
According to this adjustment device, by measuring the frequency characteristics once, it is possible to obtain a plurality of frequency characteristics when control parameters such as the gain of the motor control section and the coefficient of the filter are adjusted under a plurality of adjustment conditions. As a result, by checking multiple frequency characteristics and/or evaluation indices of multiple frequency characteristics, you can easily compare the frequency characteristics and/or evaluation indices of frequency characteristics after adjustment under different adjustment conditions, and apply the control you want to apply. Parameters can be easily selected.

(2) An adjustment device (for example, adjustment unit 400B) that adjusts control parameters of a motor control unit that controls a motor,
a frequency characteristic storage unit (for example, frequency characteristic storage unit 401) that stores the frequency characteristics of the machine measured by operating the motor control unit having control parameters before adjustment;
an adjustment condition setting section (for example, adjustment condition setting section 402) that sets a plurality of adjustment conditions for adjusting the control parameters of the motor control section;
A frequency characteristic prediction unit (e.g., frequency characteristic prediction unit 403);
Adjusting the control parameters input to the frequency characteristic prediction unit in order to optimize the control parameters using the predicted frequency characteristics and one of the plurality of adjustment conditions set by the adjustment condition setting unit. a control parameter adjustment unit (for example, control parameter adjustment unit 404),
a control parameter storage unit (for example, control parameter storage unit 405) that stores the plurality of control parameters optimized for the plurality of adjustment conditions;
a time response prediction unit (for example, time response prediction unit 409) that predicts the first time response using the predicted frequency characteristics corresponding to the optimized control parameters;
an evaluation index calculation unit (e.g., evaluation index calculation unit 410) that calculates an evaluation index of the first time response from the predicted first time response;
a presentation unit (for example, presentation unit 407) that presents at least one of the first time response and the evaluation index for each adjustment condition of the plurality of adjustment conditions;
a control parameter setting section (for example, control parameter setting section 408) that sets a control parameter selected from the plurality of control parameters stored in the control parameter storage section in the motor control section;
Adjustment device with.
According to this adjustment device, by measuring the frequency characteristics once, it is possible to obtain a plurality of frequency characteristics when control parameters such as the gain of the motor control section and the coefficient of the filter are adjusted under a plurality of adjustment conditions. As a result, by checking multiple time responses and/or multiple time response evaluation indicators predicted from multiple frequency characteristics, the time response and/or time response evaluation index after adjustment under different adjustment conditions can be evaluated. You can easily compare and select the control parameters you want to apply.

(3) The evaluation index calculation unit calculates an evaluation index of the measured frequency characteristic of the machine from the measured frequency characteristic of the machine,
The adjustment device according to (1), wherein the presenting unit presents at least one of the measured frequency characteristic of the machine and an evaluation index of the measured frequency characteristic of the machine.

(4) The time response prediction unit predicts a second time response using the measured frequency characteristics of the machine,
The evaluation index calculation unit calculates an evaluation index of the second time response from the second time response,
The adjustment device according to (2) above, wherein the presentation unit presents at least one of the second time response and the evaluation index of the second time response.

(5) The adjustment device according to any one of (1) to (4), wherein the control parameter adjustment unit optimizes the control parameters using machine learning.

(6) The adjustment device according to any one of (1) to (5), wherein the control parameter is at least one of a gain and a filter coefficient of the motor control unit.

(7) The adjustment device according to (1) or (3) above, wherein the evaluation index is at least one of a gain margin, a phase margin, and a control band.

(8) The adjustment device according to (2) or (4) above, wherein the time response is a step response or an impulse response.

(9) The adjustment device according to any one of (2), (4), or (8) above, wherein the time response evaluation index is at least one of a rise time, an overshoot amount, and a settling time. .

(10) A motor control unit (for example, motor control unit 100) that controls the motor;
The adjustment device according to any one of (1) to (9) above, which adjusts control parameters of the motor control unit;
control system with.
According to this control system, by measuring the frequency characteristics once, it is possible to obtain a plurality of frequency characteristics when control parameters such as the gain of the motor control section and the coefficient of the filter are adjusted under a plurality of adjustment conditions. As a result, by checking multiple frequency characteristics and/or multiple frequency characteristic evaluation indices, you can easily compare the frequency characteristics and/or frequency characteristic evaluation indices after adjustment under different adjustment conditions, and apply the control you want to apply. Parameters can be easily selected. Alternatively, by checking multiple time responses and/or multiple time response evaluation metrics predicted from multiple frequency characteristics, you can easily evaluate the time responses and/or time response evaluation metrics after adjustment under different adjustment conditions. You can easily select the control parameters you want to apply.

(11) a frequency generation unit that generates a signal whose frequency changes and inputs the signal to the motor control unit;
a frequency characteristic measurement unit that measures the frequency characteristics of the machine by measuring frequency characteristics of input/output gain and phase delay of the motor control unit based on the signal and the output signal of the motor control unit;
The control system according to (10) above, comprising:

(12) A control parameter adjustment method for adjusting control parameters of a motor control unit (for example, motor control unit 100) that controls a motor, the method comprising:
The computer is
a process of saving the frequency characteristics of the machine measured by operating the motor control unit having the control parameters before adjustment;
a process of setting a plurality of adjustment conditions for adjusting the control parameters of the motor control unit;
A process of predicting a frequency characteristic of the machine after adjusting the control parameter using the control parameter before and after adjustment and the saved frequency characteristic;
A process of adjusting the control parameter in order to optimize the control parameter using the predicted frequency characteristic and one of the plurality of set adjustment conditions;
a process of storing the plurality of control parameters optimized for the plurality of adjustment conditions;
A process of calculating an evaluation index of the frequency characteristic from the predicted frequency characteristic corresponding to the optimized control parameter;
A process of presenting at least one of a predicted frequency characteristic corresponding to the optimized control parameter and an evaluation index of the frequency characteristic for each adjustment condition of the plurality of adjustment conditions;
a process of setting a control parameter selected from the plurality of stored control parameters in the motor control unit;
How to adjust control parameters.
According to this control parameter adjustment method, by measuring the frequency characteristics once, it is possible to obtain multiple frequency characteristics when control parameters such as the gain of the motor control section and the coefficient of the filter are adjusted under multiple adjustment conditions. . As a result, by checking multiple frequency characteristics and/or evaluation indices of multiple frequency characteristics, you can easily compare the frequency characteristics and/or evaluation indices of frequency characteristics after adjustment under different adjustment conditions, and apply the control you want to apply. Parameters can be easily selected.

(13) A control parameter adjustment method for adjusting control parameters of a motor control unit (for example, motor control unit 100) that controls a motor, the method comprising:
The computer is
a process of saving the frequency characteristics of the machine measured by operating the motor control unit having the control parameters before adjustment;
a process of setting a plurality of adjustment conditions for adjusting the control parameters of the motor control unit;
A process of predicting a frequency characteristic of the machine after adjusting the control parameter using the control parameter before and after adjustment and the saved frequency characteristic;
A process of adjusting the control parameter in order to optimize the control parameter using the predicted frequency characteristic and one of the plurality of set adjustment conditions;
a process of storing the plurality of control parameters optimized for the plurality of adjustment conditions;
a process of predicting a first time response using predicted frequency characteristics corresponding to the optimized control parameters;
a process of calculating an evaluation index of the first time response from the predicted first time response;
a process of presenting at least one of the first time response and the evaluation index for each adjustment condition of the plurality of adjustment conditions;
a process of setting a control parameter selected from the plurality of stored control parameters in the motor control unit;
How to adjust control parameters.
According to this control parameter adjustment method, by measuring the frequency characteristics once, it is possible to obtain multiple frequency characteristics when control parameters such as the gain of the motor control section and the coefficient of the filter are adjusted under multiple adjustment conditions. . As a result, by checking multiple time responses and/or multiple time response evaluation indicators predicted from multiple frequency characteristics, the time response and/or time response evaluation index after adjustment under different adjustment conditions can be evaluated. You can easily compare and select the control parameters you want to apply.

(14) The computer:
a process of calculating an evaluation index of the measured frequency characteristic of the machine from the measured frequency characteristic of the machine;
a process of presenting at least one of the measured frequency characteristic of the machine and an evaluation index of the measured frequency characteristic of the machine;
The control parameter adjustment method according to (12) above, which performs the following.

(15) The computer:
A process of predicting a second time response using the measured frequency characteristics of the machine;
a process of calculating an evaluation index of the second time response from the second time response;
a process of presenting at least one of the second time response and an evaluation index of the second time response;
The control parameter adjustment method according to (13) above, which performs the following.

10, 10A control system 100 motor control section 110 subtracter 120 speed control section 130 filter 140 current control section 150 motor 200 frequency generation section 300 frequency

characteristic measurement section

400, 400A, 400B adjustment section 401 frequency characteristic storage section 402 adjustment condition setting section 403 Frequency characteristic prediction unit 404 Control parameter adjustment unit 405 Control parameter storage unit 406 Evaluation index calculation unit 407 Presentation unit 408 Control parameter setting unit 409 Time response prediction unit 410 Evaluation index calculation unit 500 Display screen 600 Machine learning unit 601 State information acquisition unit 602 Learning section 603 Behavior information output section 604 Value function storage section 605 Optimization behavior information output section 700 Display screen 800 Network

Claims

An adjustment device that adjusts control parameters of a motor control unit that controls a motor, the adjustment device comprising:
a frequency characteristic storage unit that stores frequency characteristics of the machine measured by operating the motor control unit having control parameters before adjustment;
an adjustment condition setting unit that sets a plurality of adjustment conditions for adjusting the control parameters of the motor control unit;
a frequency characteristic prediction unit that predicts the frequency characteristic of the machine after the control parameter is adjusted, using the control parameter before and after adjustment, and the frequency characteristic stored in the frequency characteristic storage unit;
Adjusting the control parameters input to the frequency characteristic prediction unit in order to optimize the control parameters using the predicted frequency characteristics and one of the plurality of adjustment conditions set by the adjustment condition setting unit. a control parameter adjustment section,
a control parameter storage unit that stores the plurality of control parameters optimized for the plurality of adjustment conditions;
an evaluation index calculation unit that calculates an evaluation index of the frequency characteristic from the predicted frequency characteristic corresponding to the optimized control parameter;
a presentation unit that presents at least one of the predicted frequency characteristics and the evaluation index corresponding to the optimized control parameters for each adjustment condition of the plurality of adjustment conditions;
a control parameter setting unit that sets a control parameter selected from the plurality of control parameters stored in the control parameter storage unit to the motor control unit;
Adjustment device with.
An adjustment device that adjusts control parameters of a motor control unit that controls a motor,
a frequency characteristic storage unit that stores frequency characteristics of the machine measured by operating the motor control unit having control parameters before adjustment;
an adjustment condition setting unit that sets a plurality of adjustment conditions for adjusting the control parameters of the motor control unit;
a frequency characteristic prediction unit that predicts the frequency characteristic of the machine after the control parameter is adjusted, using the control parameter before and after adjustment, and the frequency characteristic stored in the frequency characteristic storage unit;
Adjusting the control parameters input to the frequency characteristic prediction unit in order to optimize the control parameters using the predicted frequency characteristics and one of the plurality of adjustment conditions set by the adjustment condition setting unit. a control parameter adjustment section,
a control parameter storage unit that stores the plurality of control parameters optimized for the plurality of adjustment conditions;
a time response prediction unit that predicts the first time response using the predicted frequency characteristics corresponding to the optimized control parameters;
an evaluation index calculation unit that calculates an evaluation index of the first time response from the predicted first time response;
a presentation unit that presents at least one of the first time response and the evaluation index for each adjustment condition of the plurality of adjustment conditions;
a control parameter setting unit that sets a control parameter selected from the plurality of control parameters stored in the control parameter storage unit to the motor control unit;
Adjustment device with.
The evaluation index calculation unit calculates an evaluation index of the measured frequency characteristic of the machine from the measured frequency characteristic of the machine,
The adjustment device according to claim 1, wherein the presenting unit presents at least one of the measured frequency characteristic of the machine and an evaluation index of the measured frequency characteristic of the machine.
The time response prediction unit predicts a second time response using the measured frequency characteristics of the machine,
The evaluation index calculation unit calculates an evaluation index of the second time response from the second time response,
The adjustment device according to claim 2, wherein the presentation unit presents at least one of the second time response and an evaluation index of the second time response.
The adjustment device according to any one of claims 1 to 4, wherein the control parameter adjustment unit optimizes the control parameters using machine learning.
The adjustment device according to any one of claims 1 to 5, wherein the control parameter is at least one of a gain and a filter coefficient of the motor control unit.
The adjustment device according to claim 1 or 3, wherein the evaluation index is at least one of a gain margin, a phase margin, and a control band.
The adjustment device according to claim 2 or 4, wherein the time response is a step response or an impulse response.
The adjustment device according to claim 2, wherein the time response evaluation index is at least one of a rise time, an overshoot amount, and a settling time.
a motor control unit that controls the motor;
The adjustment device according to any one of claims 1 to 9, which adjusts control parameters of the motor control unit;
control system with.
a frequency generation unit that generates a signal whose frequency changes and inputs the signal to the motor control unit;
a frequency characteristic measurement unit that measures the frequency characteristics of the machine by measuring the frequency characteristics of the input/output gain and phase delay of the motor control unit based on the signal and the output signal of the motor control unit;
The control system according to claim 10, comprising:
A control parameter adjustment method for adjusting control parameters of a motor control unit that controls a motor, the method comprising:
The computer is
a process of saving the frequency characteristics of the machine measured by operating the motor control unit having the control parameters before adjustment;
a process of setting a plurality of adjustment conditions for adjusting the control parameters of the motor control unit;
A process of predicting a frequency characteristic of the machine after adjusting the control parameter using the control parameter before and after adjustment and the saved frequency characteristic;
A process of adjusting the control parameter in order to optimize the control parameter using the predicted frequency characteristic and one of the plurality of set adjustment conditions;
a process of storing the plurality of control parameters optimized for the plurality of adjustment conditions;
A process of calculating an evaluation index of the frequency characteristic from the predicted frequency characteristic corresponding to the optimized control parameter;
a process of presenting at least one of the predicted frequency characteristics and the evaluation index corresponding to the optimized control parameters for each adjustment condition of the plurality of adjustment conditions;
a process of setting a control parameter selected from the plurality of stored control parameters in the motor control unit;
How to adjust control parameters to perform.
A control parameter adjustment method for adjusting control parameters of a motor control unit that controls a motor, the method comprising:
The computer is
a process of saving the frequency characteristics of the machine measured by operating the motor control unit having the control parameters before adjustment;
a process of setting a plurality of adjustment conditions for adjusting the control parameters of the motor control unit;
A process of predicting a frequency characteristic of the machine after adjusting the control parameter using the control parameter before and after adjustment and the saved frequency characteristic;
A process of adjusting the control parameter in order to optimize the control parameter using the predicted frequency characteristic and one of the plurality of set adjustment conditions;
a process of storing the plurality of control parameters optimized for the plurality of adjustment conditions;
a process of predicting a first time response using predicted frequency characteristics corresponding to the optimized control parameters;
a process of calculating an evaluation index of the first time response from the predicted first time response;
a process of presenting at least one of the first time response and the evaluation index for each adjustment condition of the plurality of adjustment conditions;
a process of setting a control parameter selected from the plurality of stored control parameters in the motor control unit;
How to adjust control parameters.
The computer,
a process of calculating an evaluation index of the measured frequency characteristic of the machine from the measured frequency characteristic of the machine;
a process of presenting at least one of the measured frequency characteristics of the machine and an evaluation index of the measured frequency characteristics of the machine;
13. The control parameter adjustment method according to claim 12.
The computer,
A process of predicting a second time response using the measured frequency characteristics of the machine;
a process of calculating an evaluation index of the second time response from the second time response;
a process of presenting at least one of the second time response and an evaluation index of the second time response;
14. The control parameter adjustment method according to claim 13.