CN113711138A

CN113711138A - Servo control device

Info

Publication number: CN113711138A
Application number: CN202080029088.3A
Authority: CN
Inventors: 藤田智哉; 池田辽辅; 佐藤刚
Original assignee: Mitsubishi Electric Corp
Current assignee: Mitsubishi Electric Corp
Priority date: 2019-04-22
Filing date: 2020-01-08
Publication date: 2021-11-26
Anticipated expiration: 2040-01-08
Also published as: DE112020002043T5; JPWO2020217597A1; WO2020217597A1; WO2020217282A1; JP7034383B2; CN113711138B

Abstract

A servo control device (100) that feeds back a detection position (Xd) detected by a rotation angle detector (2) to an actuator that drives a machine unit (7a) to which a driven body is connected, and controls the actuator so as to follow a position command (Xc) calculated by a command value calculation unit (9) by means of a controller unit (6a), the servo control device (100) comprising: an evaluation point sensor (1a) that detects a state quantity of an evaluation point; a reference point sensor (1b) that detects a state quantity of a reference point; a first transfer function calculation unit (4a) that calculates a first transfer function (FRF1(s)) using the state quantities at the evaluation points and the state quantities at the reference points; a second transfer function calculation unit (4b) that calculates a second transfer function (FRF2(s)) using the state quantity and the detection position of the reference point; and a simulation unit (5) that calculates the estimated position of the evaluation point using the first transfer function (FRF1(s)), the second transfer function (FRF2(s)), and the stiffness value parameter (Kdr).

Description

Servo control device

Technical Field

The present invention relates to a servo control device for performing system identification.

Background

The servo control device is a device that performs feedback control using an actuator so that the position of the driven body detected using the position detector matches the commanded position.

Mechanical devices having multiple degrees of freedom, such as numerically controlled machine tools, industrial machines, robots, conveyors, and the like, have servo control devices called axes. The mechanical apparatus described above controls the position of the driven body by the actuators attached to the respective shafts, and combines the position controls of the respective shafts, thereby realizing a motion with multiple degrees of freedom.

Servo control for controlling such that a movement trajectory accurately follows a specified path, i.e., a command trajectory, is called trajectory control or contour movement control. In the trajectory control, if an error occurs in the movement of the axis due to interference such as friction and vibration of the mechanical structure, the movement trajectory deviates from the command trajectory, and thus a trajectory error occurs. For example, in a numerical control machine tool in which a shape is generated by transferring the motion of a cutting tool to a workpiece to be machined, even a trajectory error of several tens of micrometers may be determined as a machining defect.

In a numerically controlled machine tool, an industrial machine, a robot, a conveyor, or the like, a portion actually performing a work such as a tool, a conveyed object, or a robot hand is a control target for which it is really desired to realize trajectory control. However, it is difficult to attach position detectors for detecting the positions of the driven bodies to the controlled object in a completely uniform manner in all the shafts. Therefore, when the servo control device performs feedback control, if the mounting position of the position detector does not coincide with the position of the control target, the servo control device cannot accurately detect the motion of the control target by the position detector, and therefore a trajectory error occurs.

When a trajectory error occurs in the motion trajectory, error reduction is achieved by inputting a correction command to the servo control device. In order to generate the correction command, it is necessary to identify the cause of the track error and know the error amount of the track error. As a direct method of knowing the error amount, a method is known in which a displacement measuring instrument is attached to a control target and a generated trajectory error is measured. For example, a measuring device called a ball bar gauge is a device in which 2 high-precision steel balls are coupled to each other via a displacement meter, and the relative displacement between 2 points is read when a movement is performed such that the relative distance between the 2 balls is kept constant. A cue stick instrument is often used to measure a trajectory error of a tool tip of a machine tool. However, in the case where the displacement measuring instrument as described above is mounted, it is difficult to perform operations such as machining, assembly, and conveyance, and therefore the operation needs to be stopped every time measurement is performed. Therefore, a more simple method is more preferable for measuring or estimating the trajectory error of the control target during the work.

Correction of a trajectory error by a servo control device based on a result of prediction of the trajectory error using modeling of a mechanical device, system recognition, or the like, and a result of prediction of a cause of the error is an important issue, and a plurality of methods are known. For example, patent document 1 discloses a technique of obtaining a frequency characteristic of a machine from an operation amount and an operation command detected by a detection means, and identifying a rigid body load model of a load amount of the machine, a friction model in which a friction is converted into a numerical value, and a vibration characteristic model.

Patent document 1: japanese patent laid-open publication No. 2006-333594

Disclosure of Invention

However, the technique described in patent document 1 has a problem that the cause of error occurrence and the maximum error amount can be specified in the frequency domain, but an error amount occurring at the time of the control target cannot be predicted.

The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a servo control device capable of recognizing mechanical characteristics for performing trajectory control and realizing system recognition for simulating a trajectory error of a controlled object.

In order to solve the above-described problems and achieve the object, the present invention is a servo control device in which a detected position indicating a position of a driven body detected by a position detector attached to a detected point is fed back to 1 or more actuators for driving a machine unit to which the driven body to be controlled is connected, and the actuators are controlled by a controller unit so that the position of the driven body follows a position command that instructs a position calculated by a command value calculation unit. The servo control device is characterized by comprising: a first detection unit that detects a state quantity of an evaluation point set at a driven body or a position corresponding to the driven body; a second detection unit that detects a state quantity of a reference point set in the mechanical unit between the evaluation point and the actuator; a first transfer function calculation unit that calculates a first transfer function that is a frequency response characteristic from a reference point to an evaluation point, using the state quantity of the evaluation point and the state quantity of the reference point; a second transfer function calculation unit that calculates a second transfer function that is a frequency response characteristic from the detection position to the reference point, using the state quantity and the detection position of the reference point; and a first simulation unit that calculates the estimated position of the evaluation point using the first transfer function, the second transfer function, and a rigidity value parameter indicating a rigidity value between the detected position and the reference point. The first simulation unit includes: a controller simulation unit that generates a command torque estimation value for the actuator by simulating the controller unit using, as feedback, a detected point estimation position at which the behavior of the detected position is estimated; a reference point estimated position calculation unit that calculates a reference point estimated position at which the behavior of the reference point is estimated, using the second transfer function and the detected point estimated position; an evaluation point estimated position calculation unit that calculates an evaluation point estimated position at which the behavior of the evaluation point is estimated, using the first transfer function and the reference point estimated position; a drive reaction force estimation unit that calculates a drive reaction force estimation value using the detected point estimation position, the reference point estimation position, and the rigidity value parameter; a detected point estimated position calculation unit that calculates a detected point estimated position using an effective torque estimated value calculated from the drive reaction force estimated value and the command torque estimated value; and a simulator parameter setting unit that sets the first transfer function in the evaluation point estimated position calculating unit, sets the second transfer function in the reference point estimated position calculating unit, and sets the rigidity value parameter in the driving reaction force estimating unit.

ADVANTAGEOUS EFFECTS OF INVENTION

According to the present invention, the servo control device has an effect of recognizing mechanical characteristics for performing trajectory control, and realizing system recognition for simulating a trajectory error of a controlled object.

Drawings

Fig. 1 is a diagram showing a configuration example of a numerically controlled machine tool according to embodiment 1.

Fig. 2 is a schematic diagram showing a configuration example of an X-axis servo control device as an axis of the numerically controlled machine tool according to embodiment 1.

Fig. 3 is a block diagram showing a configuration example of the controller unit according to embodiment 1.

Fig. 4 is a block diagram showing a configuration example of a servo control device for performing system identification according to embodiment 1.

Fig. 5 is a block diagram showing a configuration example of the simulation unit according to embodiment 1.

Fig. 6 is a block diagram showing a configuration example of the controller simulation unit according to embodiment 1.

Fig. 7 is a block diagram showing a configuration example of the driving reaction force estimating unit according to embodiment 1.

Fig. 8 is a flowchart showing the operation of the servo control device according to embodiment 1.

Fig. 9 is a diagram showing an example in the case where the processing circuit included in the servo control device according to embodiment 1 is configured by a processor and a memory.

Fig. 10 is a diagram showing an example in the case where the processing circuit included in the servo control device according to embodiment 1 is configured by dedicated hardware.

Fig. 11 is a diagram showing a configuration example of a servo control device for performing system identification according to embodiment 2.

Fig. 12 is a schematic diagram showing a configuration example of an X-axis servo control device as an axis of the numerically controlled machine tool according to embodiment 2.

Fig. 13 is a block diagram showing a configuration example of the controller unit according to embodiment 2.

Fig. 14 is a schematic diagram showing a configuration example of a servo control device according to embodiment 3.

Fig. 15 is a diagram showing a configuration example of a servo control device for performing system identification according to embodiment 4.

Fig. 16 is a flowchart showing the operation of the servo control device according to embodiment 4.

Fig. 17 is a diagram showing a configuration example of a servo control device for performing system identification according to embodiment 5.

Fig. 18 is a block diagram showing a configuration example of the correction command calculation unit according to embodiment 5.

Fig. 19 is a block diagram showing a configuration example of the controller unit according to embodiment 5.

Fig. 20 is a flowchart showing an operation of the servo control device according to embodiment 5.

Fig. 21 is a diagram showing a configuration example of a servo control device for performing system identification according to embodiment 6.

Fig. 22 is a block diagram showing a configuration example of a servo control device for performing system identification according to embodiment 7.

Fig. 23 is a block diagram showing a configuration example of the learning unit according to embodiment 7.

Fig. 24 is a block diagram showing a configuration example of the simulation unit according to embodiment 7.

Fig. 25 is a flowchart showing an operation of the servo control device according to embodiment 7.

Fig. 26 is a diagram showing a configuration example of a servo control device for performing system identification according to embodiment 8.

Fig. 27 is a block diagram showing a configuration example of the learning unit according to embodiment 8.

Fig. 28 is a block diagram showing a configuration example of the simulation unit according to embodiment 8.

Detailed Description

The servo control device according to the embodiment of the present invention will be described in detail below with reference to the drawings. The present invention is not limited to the embodiments. The case where the servo controller performs system recognition of the numerically controlled machine tool will be specifically described, but the present invention can also be applied to machines such as industrial machines, robots, and conveyors having 1 or more servo controllers that drive a control target using an actuator.

Embodiment 1.

Fig. 1 is a diagram showing a configuration example of a numerically controlled machine tool 99 according to embodiment 1 of the present invention. The numerical control machine tool 99 is an orthogonal 3-axis vertical machine tool, and has 3 servo control devices in total, i.e., an X axis, a Y axis, and a Z axis. The numerical control machine tool 99 drives the tool 76 in the X-axis and Z-axis directions, and drives the workpiece 78 provided on the table 77 in the Y-axis direction, thereby machining the workpiece 78. That is, in the numerical control machine tool 99, the driven body to be controlled in the X axis and the Z axis is the tool 76, and the driven body to be controlled in the Y axis is the workpiece 78.

In the numerical control machine tool 99, the rotational motion of the motor 71 as an actuator is converted into a linear motion in the driving direction of each axis by the feed screw 73 in each axis. As a result, the numerical control machine tool 99 realizes a 2-degree-of-freedom motion in the XZ plane of the tool 76 and a 1-degree-of-freedom motion in the Y direction of the workpiece 78, which are combinations of the linear motions of the respective axes, thereby realizing a 3-degree-of-freedom motion in the XYZ 3-dimensional space. The numerically controlled work machine 99 rotates the cutter 76 to remove material at the portion of the work piece 78 that interferes with the cutter 76, thereby creating a 3-dimensional shape of the work piece 78. If a relative displacement occurs between the tool 76 and the workpiece 78 during machining, a machining error occurs in the workpiece 78, such as a residual cut or an overcut of the material. Therefore, the most important part for evaluating the machining accuracy of the numerical control machine tool 99 is the tip of the tool 76 in the X axis and the Z axis, and the machining point of the workpiece 78 in the Y axis.

Fig. 2 is a schematic diagram showing a configuration example of an X-axis servo control device 101 as an axis of the numerically controlled machine tool 99 according to embodiment 1. Here, for convenience, only the X-axis servo control device 101 is shown, but the Y-axis and Z-axis servo control devices have the same configuration. However, the X-axis and Z-axis driven bodies are the tool 76, but are different in that the Y-axis driven body is the workpiece 78. There is a Z-axis between the X-axis and the tool 76, but the driving direction of the X-axis and the driving direction of the Z-axis are orthogonal without interfering with each other. Therefore, the Z-axis is regarded as 1 of the independent structural members when viewed from the X-axis, and is not shown.

In the X-axis servo control device 101, the rotational motion of the motor 71 is transmitted to the feed screw 73 via the coupling 74, and is converted into a linear motion via the nut 80. The linear movement of the feed screw 73 is constrained by

support bearings

75a, 75 b. The linear movement of the nut 80 drives the tool 76 in the X direction via a mechanical structural member 72, such as a Z-axis, which is commonly referred to as a support member, interposed between the tool 76 and the nut 80.

The X-axis position command Xc is output from the command value calculation unit 9 and input to the controller unit 6 a. The position command Xc indicates the position of the driven object in the desired control state calculated by the command value calculation unit 9. The controller 6a performs feedback control so that an error between a detected position Xd obtained by multiplying a motor rotation angle detected by a rotation angle detector 2 attached to the motor 71 by a pitch of the feed screw 73 and a position command Xc becomes small, and outputs a motor current Im to the motor 71 to drive the machine unit 7 a. A driven body to be controlled is connected to the machine section 7 a. Here, the rotation angle detector 2 detects only the rotation angle of the motor 71, but the rotational motion and the translational motion can be easily converted as described above. Therefore, in the present embodiment, the rotation angle detector 2 multiplies the motor rotation angle by the pitch of the feed screw 73, and outputs the detection position Xd converted into the linear movement of the X-axis servo control device 101. The rotation angle detector 2 is a position detector attached to the motor 71, i.e., a detection point.

Fig. 3 is a block diagram showing a configuration example of the controller unit 6a according to embodiment 1. The controller unit 6a outputs the motor current Im using the position command Xc input from the command value calculation unit 9 and the detection position Xd input from the rotation angle detector 2. First, the addition/subtraction operator 61a calculates a positional deviation (Xc-Xd) which is a difference between the position command Xc and the detected position Xd. The position controller 62 performs position control corresponding to the position deviation (Xc-Xd) to generate a speed command Vc. An example of a location controller 62 is a P (Proport) controller. The speed calculator 65 generates the detection speed Vd from the detection position Xd. An example of the velocity operator 65 is a differential operator. The addition/subtraction unit 61b calculates a speed deviation Vde, which is a difference between the speed command Vc and the detection speed Vd, as Vc-Vd. Speed controller 63 performs speed control in accordance with speed deviation Vde to generate current command Ic. An example of the speed controller 63 is a pi (proportional integral) controller. The addition/subtraction arithmetic unit 61c calculates a current deviation (Ic-Im) which is a difference between the current command Ic and the motor current Im. Finally, the current controller 64 performs current control in accordance with the current deviation (Ic-Im) and outputs the motor current Im. One example of current controller 64 is a PI controller.

As described above, the controller portion 6a performs control so that the detected position Xd coincides with the position indicated by the position command Xc by feedback control. However, in the numerical control machine tool 99, the mounting position of the rotation angle detector 2 cannot be matched with the tip of the tool 76 to be controlled. Therefore, when a disturbance that cannot be detected by the rotation angle detector 2 occurs in the controlled object, or when the feedback control of the controller unit 6a cannot catch up with the disturbance or the like, an unexpected error occurs in the movement of the tool 76 that is the controlled object in the numerically controlled machine tool 99.

As examples of the disturbance that affects the machining, an error due to vibration of the coupling 74, the feed screw 73, the mechanical structural member 72, and the like, and a friction force of the shaft, and the like are known. It is known that these disturbances have characteristics that change due to the position of another shaft interposed between the tool 76 and the nut 80, the mass of the tool 76 and the workpiece 78, the change over time of the machine, the wear of the feed screw 73 and the nut 80, the amount of lubricating oil in each movable shaft, the change in air temperature, and the like.

Since the disturbance varies according to the usage situation as described above, in order to maintain the machining quality of the workpiece 78 in the numerically controlled machine tool 99, the command value calculation unit 9 needs to perform control such as appropriately changing the control parameters of the controller unit 6a to correct the disturbance. Therefore, it is preferable to predict the error that continues to occur in the controlled object during the operation of the numerically controlled machine tool 99.

Fig. 4 is a block diagram showing a configuration example of the servo control device 100 for performing system identification according to embodiment 1. The servo control device 100 includes an evaluation point sensor 1a, a reference point sensor 1b, a rotation angle detector 2, a first transfer function calculation unit 4a, a second transfer function calculation unit 4b, a simulation unit 5, a controller unit 6a, a machine unit 7a, a rigidity value storage unit 8, and a command value calculation unit 9. The servo control device 100 feeds back a detected position Xd indicating the position of the driven body detected by the rotation angle detector 2 to 1 or more motors 71 that drive the machine unit 7a so that the position of the tool 76 as the driven body follows the position command Xc, and controls the motors 71 by the controller unit 6 a.

The servo control device 100 has 2 3-axis acceleration sensors. With the above configuration, the servo control device 100 can measure the dynamic characteristics of the machine during operation. The 2 3-axis acceleration sensors are the same type of acceleration sensor, and for convenience, the evaluation point sensor 1a and the reference point sensor 1b are referred to as different sensors. As shown in fig. 2, the 3-axis acceleration sensor is attached to a spindle end surface 83 as an attachment portion of the tool 76 of the numerical control machine tool 99 at an attachment position, and the reference point sensor 1a is attached to a nut block 79 as a support member of the nut 80. The mounting position of the evaluation point sensor 1a is set as an evaluation point, and the mounting position of the reference point sensor 1b is set as a reference point. The evaluation point sensor 1a may be referred to as a first detection unit, and the reference point sensor 1b may be referred to as a second detection unit. The evaluation point is preferably coincident with the control target, but since the tool 76 rotates in the case of the X axis, if an acceleration sensor is attached here, the acceleration cannot be measured accurately. Therefore, the spindle end surface 83, which is a non-rotating portion closest to the tool 76, is set as an evaluation point in the X axis. On the other hand, since the workpiece 78 does not rotate if it is the Y axis, the workpiece 78 or the upper surface of the table 77 closest to the machining point may be set as the evaluation point, and an acceleration sensor may be provided. The evaluation point can be changed to an arbitrary position if it is on a component that is regarded as the same error amount as the controlled object. As described above, the evaluation point is set at a position corresponding to the driven body or the driven body.

Since the 3-axis acceleration sensor can measure the acceleration in the orthogonal 3-axis direction by 1 sensor, the acceleration in 3 dimensions can be measured by using 1 3-axis acceleration sensor. However, since only the acceleration signal in the X direction, which is the driving direction, is used for system recognition in the X-axis servo control device 101, the 1-axis acceleration sensor does not affect the system. Further, a 3-axis acceleration sensor may be provided at the evaluation point, signals in the X-direction and the Z-direction may be used in the X-axis and Z-axis servo control devices, respectively, and a 1-axis acceleration sensor may be provided for each axis at the reference point. In a numerically controlled work machine 99 having multiple axes, a different reference point may be selected for each axis.

In the servo control device 100, there are fixing methods for attaching the 3-axis acceleration sensor, for example, by adhesion using a magnetic force generated by a magnet, fastening using a jig and a screw, fixing by paraffin, and using an adhesive. In the present embodiment, an example in which a 3-axis acceleration sensor is used for the evaluation point sensor 1a and the reference point sensor 1b is shown, but if the movement of the evaluation point and the reference point can be measured, a speed measuring instrument such as a laser doppler vibrometer, a laser displacement sensor, or a displacement measuring instrument such as a laser interferometer may be used.

The reference point can be set at any position if it is on the path of the driving force transmission between the motor 71 and the evaluation point. However, the coupling 74, the feed screw 73, and the like perform rotational movement. Therefore, when setting the reference point on the coupler 74, the feed screw 73, or the like, it is necessary to attach a rotation angle detector to the coupler 74, the feed screw 73, or the like, instead of the 3-axis acceleration sensor, and calculate the angular acceleration from the rotation angle, or to provide a gyro sensor capable of directly measuring the angular acceleration.

As shown in fig. 2, in the X-axis servo control device 101, the controller unit 6a performs feedback control in accordance with the position command Xc generated by the command value calculation unit 9, and drives the machine unit 7 a. At this time, the evaluation point sensor 1a detects an acceleration signal in the X direction of the evaluation point. The reference point sensor 1b detects an acceleration signal in the X direction of the reference point. The evaluation point sensor 1a and the reference point sensor 1b can detect a state other than acceleration if they indicate the motion state of the driven body. The value indicating the motion state of the driven body detected by the evaluation point sensor 1a and the reference point sensor 1b may be referred to as a state quantity.

Acceleration signals in the X direction of the evaluation point sensor 1a and the reference point sensor 1b attached to the machine unit 7a are input to the first transfer function calculation unit 4 a. The acceleration signal in the X direction of the reference point sensor 1b and the detection position Xd output from the rotation angle detector 2 attached to the motor 71 are input to the second transfer function calculation unit 4 b. The first transfer function calculation unit 4a uses the reference point acceleration Ar, which is the acceleration signal obtained from the reference point sensor 1b, and the evaluation point acceleration Ae, which is the acceleration signal obtained from the evaluation point sensor 1a, as the pairs from the reference point position Xr to the evaluation point position XrThe first transfer function FRF1(s) of the frequency response characteristic up to the point position Xe is calculated as Xe (s)/xr(s). Where s is the Laplace operator. The second transfer function calculation unit 4b calculates a second transfer function FRF2(s) ═ Xr (s)/Xd(s) which is a frequency response characteristic from the detection position Xd to the reference point position Xr, using the detection acceleration Ad obtained by differentiating the detection position Xd detected by the rotation angle detector 2 by the 2 nd order and the reference point acceleration Ar obtained from the reference point sensor 1 b. Here, an example of the transfer function calculated by the first transfer function calculating unit 4a and the second transfer function calculating unit 4b is shown by the equation (1). N and m determining the degree of the transfer function are integers, a₀、…、a_nAnd b₀、…、b_mIs the coefficient of the term of each degree of the transfer function.

[ formula 1 ]

If the mechanical structural member 72 is a completely rigid body, Xd-Xr-Xe is used, but the actual mechanical structure cannot be approximated to a completely rigid body, and therefore the vibration transmission characteristics and response characteristics at each point are different. However, the algorithm for the transfer function calculation may be any known algorithm for the transfer function calculation of a 1-input 1-output system having 1 time-series input signal and output signal, such as a least square method, a successive least square method, arx (auto regression with exologues) recognition, eigenvalue decomposition, or the like.

The simulation unit 5 estimates the position Xe at the evaluation point corresponding to the position command Xc input from the command value calculation unit 9 at any time, using the first transfer function FRF1(s), the second transfer function FRF2(s), and the stiffness value parameter Kdr stored in advance in the stiffness value storage unit 8₁And a detected point estimated position Xd₁And the calculation is performed and output to the display 10. Here, the stiffness value parameter Kdr is a stiffness value between the detection position Xd and the reference point position Xr, which are measured in advance and stored in the stiffness value storage unit 8. The stiffness value parameter Kdr is a scalar quantity.

Fig. 5 is a block diagram showing a configuration example of the simulation unit 5 according to embodiment 1. The simulation unit 5 includes a controller simulation unit 51, a detected point estimated position calculation unit 53, a reference point estimated position calculation unit 54, a driving reaction force estimation unit 55, an evaluation point estimated position calculation unit 56, a simulation parameter setting unit 57, and an addition/subtraction unit 61 e. The controller simulation unit 51 simulating the controller unit 6a uses the position command Xc and the detected point estimated position Xd₁Estimated value Tm of motor torque₁And (6) performing operation. Fig. 6 is a block diagram showing a configuration example of the controller simulation unit 51 according to embodiment 1. The controller simulation unit 51 differs from the controller unit 6a in that the detected point estimated position Xd is used instead of the detected position Xd as a feedback signal₁And relative to the output Im of the current controller 64₁And outputs the motor torque estimated value Tm calculated by the torque estimator 70₁. As described above, the controller simulation unit 51 estimates the detected point estimated position Xd at which the behavior of the detected position Xd is estimated₁As feedback, the controller unit 6a is simulated to generate a motor torque estimated value Tm, which is an estimated value of a command torque to the motor 71₁. Estimated motor torque value Tm₁One example of (b) is an estimated value of a torque constant of the motor 71.

Returning to the description of fig. 5. The addition/subtraction arithmetic unit 61e estimates the motor torque from the command torque, which is an estimated value of the motor torque₁And driving reaction force estimated value Tr₁For the effective torque estimate (Tm)₁－Tr₁) And (6) performing operation. The detected point estimated position calculation unit 53 uses the effective torque estimated value (Tm)₁－Tr₁) Estimating the position Xd of the detected point₁And (6) performing operation. The reference point estimated position calculation unit 54 uses the second transfer function FRF2(s) and the detected point estimated position Xd₁Estimating the position Xr of the reference point for estimating the behavior of the reference point₁And (6) performing operation. The evaluation point estimated position calculation unit 56 estimates the position Xr using the first transfer function FRF1(s) and the reference point₁Estimating the position Xe of the evaluation point for estimating the behavior of the evaluation point₁And (6) performing operation. Drive reaction force estimating unit 55 estimates position Xd using the detected point₁And the reference point estimated position Xr₁And a stiffness value parameter Kdr, an estimated value Tr for the driving reaction force₁And (6) performing operation.

With the above configuration, the simulation unit 5 can measure the relative movement locus between the detection position Xd and the driven body at 2 points. The simulation unit 5 may perform laplace transform on an input signal in a time domain, multiply the input signal by a transfer function in a frequency domain, and then perform operation on an output in the time domain in inverse laplace transform, with respect to an operation method using an evaluation point and a reference point of the first transfer function FRF1(s) and the second transfer function FRF2(s), and may describe a transfer function by a difference equation of a discrete system using a bilinear transform method such as Tustin transform in an operation cycle of the controller unit 6a, and perform operation on an output signal with respect to the input signal.

In the present embodiment, only the motion in the motion direction at the evaluation point is set as the object of calculation, but for example, a transfer function from the motor current Im to the acceleration in the direction orthogonal to the driving direction such as the Y direction and the Z direction at the evaluation point may be calculated, and the estimated positions in the Y direction and the Z direction at the evaluation point may be calculated based on the motor current Im in the simulation unit 5.

Fig. 7 is a block diagram showing a configuration example of the driving reaction force estimating unit 55 according to embodiment 1. The addition/subtraction operator 61d estimates the position Xr of the reference point₁And a detected point estimated position Xd₁Difference (Xd) of (A)₁-Xr 1). The multiplier 67 multiplies the stiffness value parameter Kdr by the difference (Xd)₁-Xr 1) and an estimated value Tr for the driving reaction force₁And (6) performing operation. On the other hand, the drive reaction force estimation unit 55 may use a transfer function describing transfer characteristics of the drive reaction force Tr with respect to the difference (Xd-Xr) between the detected position Xd and the reference position Xr, instead of the stiffness value parameter Kdr. The stiffness value parameter Kdr is a parameter that applies a load to a reference point in advance, and can be calculated from the magnitude of the applied load force and the displacement amounts of the reference point and the detection point at that time.

Returning to the description of fig. 5. The simulation parameter setting unit 57 sets the first transfer function FRF1(s) to the evaluation point estimated position calculation unit 56, sets the second transfer function FRF2(s) to the reference point estimated position calculation unit 54, and sets the stiffness value parameter Kdr to the driving reaction force estimation unit 55. The timing at which the parameter setting unit 57 sets the parameter may be continuous for any time, and may be set by detecting the timing at which the motor 71 completely stops. The values of the parameters set by the simulation parameter setting unit 57 may be discontinuous values, or may be changed with interpolation or a time constant so that the variation of the parameters becomes continuous.

The display 10 is used for estimating the evaluation point estimated position Xe outputted from the simulation unit 5₁And a detected point estimated position Xd₁A video monitor presented to a machine operator operating the machine. The display 10 estimates a position Xd from a detected point for each axis of the numerically controlled machine tool 99, for example₁Corresponding evaluation point estimated position Xe₁The error amount of (2) is displayed, and thus the error amount generated in the numerical control machine 99 can be presented. The display 10 estimates the movement locus and the evaluation point estimated position Xe at the detection point position when the synchronous control is performed by the X axis, the Y axis, and the Z axis₁The motion trajectory of the point (c) is plotted on a 2-dimensional plane or a 3-dimensional space, and thus, it is possible to indicate to the machine operator what machining error occurs in the entire machine. Thus, the machine operator can determine whether or not machining is acceptable by checking the content displayed on the display 10, and can perform correction machining on the portion where the error occurs, or can correct the machining program, thereby suppressing the error.

The operation of the servo control device 100 will be described with reference to a flowchart. Fig. 8 is a flowchart showing the operation of the servo control device 100 according to embodiment 1. In the servo control device 100, the evaluation point sensor 1a detects the state quantity of the evaluation point (step S1). The reference point sensor 1b detects the state quantity of the reference point (step S2). The first transfer function calculation unit 4a calculates a first transfer function FRF1(S) that is a frequency response characteristic from the reference point to the evaluation point, using the state quantity of the evaluation point and the state quantity of the reference point (step S3). The second transfer function computing unit 4b uses parametersThe second transfer function FRF2(S), which is a frequency response characteristic from the detected position Xd to the reference point, is calculated according to the state quantity of the point and the detected position Xd (step S4). The simulation unit 5 estimates the evaluation point estimated position Xe representing the estimated position of the evaluation point using the first transfer function FRF1(s), the second transfer function FRF2(s), the position command Xc, and the rigidity value parameter Kdr representing the rigidity value between the detected position Xd and the reference point₁And a detected point estimated position Xd indicating an estimated position of the detected position Xd₁An operation is performed (step S5).

Next, a hardware configuration of the servo control device 100 will be explained. In the servo control device 100, the evaluation point sensor 1a, the reference point sensor 1b, and the rotation angle detector 2 are measurement devices. The mechanical portion 7a is a device having an actuator and the like. The rigidity value storage unit 8 is a memory. The first transfer function arithmetic unit 4a, the second transfer function arithmetic unit 4b, the simulation unit 5, the controller unit 6a, and the command value arithmetic unit 9 are realized by a processing circuit. The processing circuit may be a processor and a memory that execute a program stored in the memory, or may be dedicated hardware.

Fig. 9 is a diagram showing an example in the case where the processing circuit included in the servo control device 100 according to embodiment 1 is configured by a processor and a memory. In the case where the processing circuit is constituted by the processor 91 and the memory 92, each function of the processing circuit of the servo control device 100 is realized by software, firmware, or a combination of software and firmware. The software or firmware is described as a program and stored in the memory 92. In the processing circuit, the processor 91 reads and executes a program stored in the memory 92, thereby realizing each function. That is, the processing circuit has a memory 92, and the memory 92 stores a program for finally executing the processing of the servo control device 100. The programs can be said to cause a computer to execute the procedure and method of the servo control apparatus 100.

Here, the processor 91 may be a cpu (central Processing unit), a Processing device, an arithmetic device, a microprocessor, a microcomputer, a dsp (digital Signal processor), or the like. The memory 92 is, for example, a nonvolatile or volatile semiconductor memory such as a ram (random Access memory), a rom (read Only memory), a flash memory, an EPROM (erasable Programmable rom), and an EEPROM (Electrically EPROM), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, or a dvd (digital Versatile disc).

Fig. 10 is a diagram showing an example in the case where the processing circuit included in the servo control device 100 according to embodiment 1 is configured by dedicated hardware. In the case where the processing circuit is formed by dedicated hardware, the processing circuit 93 shown in fig. 10 is, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an asic (application Specific Integrated circuit), an fpga (field Programmable Gate array), or a combination thereof. The respective functions of the servo control device 100 may be realized by the processing circuit 93 according to the function type, or the respective functions may be collectively realized by the processing circuit 93.

Further, each function of the servo control device 100 may be partly implemented by dedicated hardware and partly implemented by software or firmware. As described above, the processing circuit can implement the functions described above by dedicated hardware, software, firmware, or a combination thereof.

As described above, according to the present embodiment, even when the position of the evaluation point cannot be directly measured, the servo control device 100 that performs system recognition can calculate the transfer function using the 2 acceleration sensors and the rotation angle detector 2, and can estimate the position of the evaluation point at the moment with high accuracy using the calculated transfer function and the position command Xc. The servo control device 100 branches the signals of the evaluation point sensor 1a and the reference point sensor 1b as acceleration sensors and the signal of the rotation angle detector 2 as a position detector into 2 or more frequency bands and corrects the signals, thereby being capable of measuring the motion trajectory of the driven body with high accuracy.

The servo control device 100 can recognize the mechanical characteristics by a simple installation in a machine that performs trajectory control such as a numerically controlled machine tool, an industrial machine, a robot, or a conveyor, and can perform system recognition that simulates a trajectory error of a controlled object. Further, since the servo control device 100 uses an accelerometer in measuring the state of the evaluation point as the control target, the machine in use can measure the state without removing the workpiece 78, the tool 76, and the like.

Embodiment 2.

In embodiment 2, a case where the servo controller performs the full closed-loop control will be described.

Fig. 11 is a diagram showing a configuration example of a servo control device 100b for performing system identification according to embodiment 2. The servo control device 100b according to embodiment 2 differs from the servo control device 100 according to embodiment 1 in that a linear motion position detector 21 is used instead of the rotation angle detector 2 as an input of a detection position, a mechanical unit 7b having a different configuration from that of embodiment 1 is used, and a controller unit 6b is used for controlling the mechanical unit 7 b.

Fig. 12 is a schematic diagram showing a configuration example of an X-axis servo control device 101b as an axis of the numerically controlled machine tool 99 according to embodiment 2. The X-axis servo control device 101b includes a linear position detector 21 called a linear encoder for detecting the linear position of the nut block 79. The mechanical configuration such as the X-axis servo control device 101b is referred to as full closed-loop control.

Fig. 13 is a block diagram showing a configuration example of the controller unit 6b according to embodiment 2. In the fully closed loop controlled servo control device 100b, the controller portion 6b uses the signal of the translational position detector 21 as a position feedback signal and uses the signal of the rotation angle detector 2 as a speed feedback signal. The others are the same as the controller portion 6 a. In fig. 11 to 13, the signal of the linear motion position detector 21, that is, the detection position detected by the linear motion position detector 21 is represented by Xl.

In embodiment 2, as shown in fig. 12, the position of the evaluation point is the same as that in embodiment 1, but the position of the detection point of the machine unit 7b is the mounting position of the linear motion position detector 21. The position of the reference point is set on the surface of the machine structural member 72 between the detection point and the evaluation point. Since only the mounting positions of the sensors used are different, the first transfer function arithmetic unit 4a, the second transfer function arithmetic unit 4b, and the simulation unit 5 function in the same manner as in embodiment 1.

In embodiment 2, although the description has been given of the application to the servo control device 100b driven by the motor 71 and the feed screw 73 as the rotary motor as an example, the present embodiment can be applied to a configuration without the rotary motor as in a servo control device using a linear motor as an actuator.

The operation flow and hardware configuration of the servo control device 100b are the same as those of the servo control device 100 according to embodiment 1.

As described above, according to the present embodiment, the servo control device 100b that performs system recognition can estimate the position of the evaluation point at the time with high accuracy even in the case of the full-closed loop control. The servo control device 100b can be applied to any control method such as the semi-closed loop control and the full-closed loop control.

Embodiment 3.

In embodiment 1 and embodiment 2, an application example to a servo control device as a linear feed axis is shown, but a numerical control machine tool may have a rotary axis. In embodiment 3, a case where the present invention is applied to a servo control device as a rotary shaft will be described.

Fig. 14 is a schematic diagram showing a configuration example of a servo control device 100c according to embodiment 3. The servo control device 100c according to embodiment 3 is different from the servo control device 100 according to embodiment 1 in that a mechanical unit 7c having a different configuration from that of embodiment 1 is used. The motor 71 decelerates the rotational movement via the worm gear 81 to rotate the rotary table 82. Since the workpiece 78 is provided on the machine structural member 72 at the rotation axis of the servo control device 100c shown in fig. 14, the evaluation point of the rotation axis is located on the machine structural member 72. In the servo control device 100c shown in fig. 14, the reference point is 1 point on the circumference of the rotary table 82. In the servo control device 100c, the rotation direction of the rotary table 82 is indicated by an arrow 84 in fig. 14.

In the servo control device 100c shown in fig. 14, since the rotation axis performs a rotational motion, the motion direction at the evaluation point and the reference point becomes the circumferential direction of the rotational motion. Therefore, in the acceleration sensor orthogonal to the 3 axes, it is difficult to accurately measure the rotational motions of the reference point and the evaluation point. In embodiment 3, instead of the acceleration sensor, a gyro sensor 1c capable of detecting a rotational motion is provided at the evaluation point, and the same gyro sensor 1d is provided at the reference point, thereby measuring the angular acceleration in the circumferential direction. As described above, the system identification method described in embodiment 1 can be applied.

In addition, although a system is known in which 2 acceleration sensors are provided at point-symmetric positions that become the rotation center in addition to the gyro sensor in the configuration of the servo control device 100c shown in fig. 14, thereby accurately measuring the movement in the circumferential direction, the configuration is not limited to this if the system or the measuring instrument can measure the respective accelerations in the circumferential direction.

In addition to the worm drive, there is a mechanical structure called a direct drive system that corresponds to a linear motor driven servo control device in the linear feed shaft.

The operation flow and hardware configuration of the servo control device 100c are the same as those of the servo control device 100 according to embodiment 1.

As described above, according to the present embodiment, even when the servo control device 100c that performs system recognition has a rotation axis, it is possible to estimate the position of the evaluation point at any time with high accuracy.

Embodiment 4.

In embodiment 4, a case where the control parameter of the controller unit 6a is changed will be described.

Fig. 15 is a diagram showing a configuration example of a servo control device 100d for performing system identification according to embodiment 4. The servo control device 100d according to embodiment 4 is configured such that a control parameter changing unit 11 is added to the servo control device 100 according to embodiment 1.

The control parameter changing unit 11 uses the evaluation points calculated by the simulation unit 5Estimating position Xe₁And a detected point estimated position Xd₁The control parameter CP of the controller unit 6a is changed. That is, the control parameter changing unit 11 changes the control parameter CP of the controller unit 6a so that the movement accuracy of the driven body falls within the target value. Specifically, the control parameter changing unit 11 changes the control gain of each controller of the controller unit 6 a. The control parameter changing unit 11 estimates the position Xe at the evaluation point, for example₁And a detected point estimated position Xd₁When the difference of (b) is a certain threshold value, control such as lowering the P gain of the position controller 62 is performed. The control parameter changing unit 11 can similarly change the P gain or the I gain of the speed controller 63. Further, the control parameter changing unit 11 may estimate the position Xe only by using the evaluation points₁The control parameter CP of the controller unit 6a is changed.

The operation of the servo control device 100d will be described with reference to a flowchart. Fig. 16 is a flowchart showing the operation of the servo control device 100d according to embodiment 4. The operations from step S1 to step S5 are the same as those of the servo control device 100 according to embodiment 1 shown in the flowchart of fig. 8. In the servo control device 100d, the control parameter changing unit 11 estimates the position Xe using the evaluation point₁Or the estimated position Xe of the evaluation point₁And a detected point estimated position Xd₁The control parameter CP of the controller unit 6a is changed so that the movement accuracy of the tool 76 as the driven body falls within the target value (step S11). The controller 6a performs current control in accordance with the current deviation (Ic-Im) using the control parameter CP changed by the control parameter changing unit 11, and outputs the motor current Im (step S12).

The hardware configuration of the servo control device 100d is realized by a processing circuit in the same manner as the simulation unit 5 and the like. The processing circuit may be a processor and a memory that execute a program stored in the memory, or may be dedicated hardware.

As described above, according to the present embodiment, the servo control device 100d that performs system recognition can change the control parameter CP of the controller unit 6a based on the result of estimating the temporal position of the evaluation point, thereby making it possible to achieve high accuracy of actual control. The servo control device 100d can adaptively change the control parameter by the control parameter changing unit 11 to optimize the control.

Embodiment 5.

In embodiment 5, a case where a correction command for the controller unit is calculated will be described.

Fig. 17 is a diagram showing a configuration example of a servo control device 100e for performing system identification according to embodiment 5. The servo control device 100e according to embodiment 5 includes a controller unit 6e in place of the controller unit 6a, and further includes a correction command calculation unit 12, as compared to the servo control device 100 according to embodiment 1.

The correction command calculation unit 12 estimates the position Xe using the evaluation point calculated by the simulation unit 5₁And a detected point estimated position Xd₁And, using the position command Xc calculated by the command value calculation unit 9, the position correction command Xcmp, the speed correction command Vcmp, and the current correction command Icmp for the controller unit 6e are calculated and output to the controller unit 6 e. Fig. 18 is a block diagram showing a configuration example of the correction instruction arithmetic unit 12 according to embodiment 5. First, the detected point position gain multiplier 69a estimates the detected point position Xd₁Kp × Xd multiplied by the detected point position gain Kp₁And (6) performing operation. The addition/subtraction arithmetic unit 61f estimates the position Xe on the evaluation point₁And Kp × Xd calculated by the detected point position gain multiplying unit 69a₁Difference (Xe) of₁－Kp×Xd₁) And (6) performing operation. Next, the position command gain multiplying unit 69b calculates Kc × Xc by multiplying the position command Xc by the position command gain Kc. The addition/subtraction arithmetic unit 61g adds/subtracts the difference (Xe) calculated by the addition/subtraction arithmetic unit 61f₁－Kp×Xd₁) And the difference (Xe) between Kc × Xc calculated by the position command gain multiplier 69b₁－Kp×Xd₁-Kc × Xc).

Next, the filter unit 68 filters the specific frequency component to calculate the correction command. The filter unit 68 is any one of a low-pass filter, a high-pass filter, a band-pass filter, and a band-stop filter, or a combination thereof. The position correction gain multiplier 69c outputs a value obtained by multiplying the correction command calculated by the filter unit 68 by the position correction gain as a position correction command Xcmp. The speed calculator 65 calculates the correction command calculated by the filter unit 68 in the speed dimension. One example of the velocity operator 65 is a differentiator. The speed correction gain multiplication unit 69d outputs a value obtained by multiplying the correction command for the speed dimension calculated by the speed calculator 65 by the speed correction gain as the speed correction command Vcmp. The acceleration calculator 66 calculates the correction command calculated by the filter unit 68 in the acceleration dimension. An example of the acceleration arithmetic unit 66 is a 2 nd order differentiator. The current correction gain multiplier 69e outputs a value obtained by multiplying the correction command for the acceleration dimension calculated by the acceleration calculator 66 by the current correction gain as the current correction command Icmp.

In addition, each gain is a constant value and is a real number including 0. For example, when only the position correction command Xcmp is output from the correction command calculation unit 12, the speed correction gain and the current correction gain may be set to 0. Further, the correction command calculation unit 12 may estimate the position Xe only by using the evaluation points₁The position correction command Xcmp, the speed correction command Vcmp, and the current correction command Icmp of the opposing controller unit 6e are calculated and output to the controller unit 6 e. The correction command calculation unit 12 may calculate at least 1 of the position correction command Xcmp, the speed correction command Vcmp, and the current correction command Icmp, and output the calculated value to the controller unit 6 e.

The controller unit 6e receives each correction command from the correction command calculation unit 12 and performs feedback control. Fig. 19 is a block diagram showing a configuration example of the controller unit 6e according to embodiment 5. The difference from the controller unit 6a of embodiment 1 is the calculation contents of the addition/subtraction calculator 61a, the addition/subtraction calculator 61b, and the addition/subtraction calculator 61 c. Specifically, the addition/subtraction arithmetic unit 61a calculates the positional deviation (Xc-Xd + Xcmp) based on the position command Xc, the detected position Xd, and the position correction command Xcmp. The addition/subtraction unit 61b calculates the speed deviation Vde as Vc-Vd + Vcmp based on the speed command Vc, the detected speed Vd, and the speed correction command Vcmp. The addition/subtraction arithmetic unit 61c calculates the current deviation (Ic-Im + Icmp) based on the current command Ic, the motor current Im, and the current correction command Icmp.

The operation of the servo control device 100e will be described with reference to a flowchart. Fig. 20 is a flowchart showing the operation of the servo control device 100e according to embodiment 5. The operations from step S1 to step S5 are the same as those of the servo control device 100 according to embodiment 1 shown in the flowchart of fig. 8. In the servo control device 100e, the correction command calculation unit 12 calculates a correction command (step S21). Specifically, the correction command calculation unit 12 estimates the position Xe using the evaluation points₁Or the estimated position Xe of the evaluation point₁And a detected point estimated position Xd₁At least 1 of the position correction command Xcmp, the speed correction command Vcmp, and the current correction command Icmp of the counter controller unit 6e is calculated. The controller unit 6e outputs the motor current Im using the correction command calculated by the correction command calculation unit 12 (step S22). Specifically, the controller unit 6e performs current control in accordance with the current deviation (Ic-Im + Icmp) using at least 1 of the position correction command Xcmp, the speed correction command Vcmp, and the current correction command Icmp, and outputs the motor current Im.

The hardware configuration of the servo control device 100e is realized by a processing circuit in the same manner as the simulation unit 5 and the like. The processing circuit may be a processor and a memory that execute a program stored in the memory, or may be dedicated hardware.

As described above, according to the present embodiment, the servo control device 100e for performing the system identification and the evaluation point estimated position Xe estimated by the simulation₁Accordingly, a correction command is generated, whereby highly accurate movement can be achieved. The servo control device 100e can generate a correction command by the above-described configuration, and can perform direct feedback to perform correction with high accuracy.

Embodiment 6.

In embodiment 6, a case where servo control devices for performing system identification are distributed and installed at physically separated locations will be described.

Fig. 21 is a diagram showing a configuration example of a servo control device 100f for performing system identification according to embodiment 6. The servo control device 100f is different from the servo control device 100e according to embodiment 5 in that it includes a sensor signal transmitting unit 30, a correction command transmitting unit 31, a sensor signal receiving unit 32, a correction command receiving unit 33, and a simulation command value calculating unit 90.

The sensor signal transmitting unit 30 is a first transmitting unit provided in the numerically controlled machine tool 99. The sensor signal transmitting unit 30 acquires an evaluation point acceleration Ae which is an acceleration signal from the evaluation point sensor 1a, acquires a reference point acceleration Ar which is an acceleration signal from the reference point sensor 1b, and acquires a detection position Xd detected by the rotation angle detector 2. The sensor signal transmitting unit 30 transmits the acquired evaluation point acceleration Ae, reference point acceleration Ar, and detection position Xd to a sensor signal receiving unit 32 remotely installed via a network. The sensor signal receiving unit 32 is a first receiving unit that, upon receiving the evaluation point acceleration Ae, the reference point acceleration Ar, and the detection position Xd, outputs the evaluation point acceleration Ae and the reference point acceleration Ar to the first transfer function computing unit 4a, and outputs the reference point acceleration Ar and the detection position Xd to the second transfer function computing unit 4 b. The sensor signal transmitter 30 and the sensor signal receiver 32 may be physically connected by a network cable, may be connected by wireless network communication, or may be connected by a combination of a network cable and wireless network communication.

The correction command transmitting unit 31 is a second transmitting unit that acquires at least 1 of the position correction command Xcmp, the speed correction command Vcmp, and the current correction command Icmp from the correction command computing unit 12, and transmits the acquired command to the remotely-provided correction command receiving unit 33 via the network. The correction command receiving unit 33 is a second receiving unit that outputs the position correction command Xcmp, the speed correction command Vcmp, and the current correction command Icmp to the controller unit 6e if at least 1 of them is received. The correction command transmitting unit 31 and the correction command receiving unit 33 may be physically connected by a network cable, may be connected by wireless network communication, or may be connected by a combination of a network cable and wireless network communication.

When the simulation unit 5 is remotely installed, there is a problem in that the simulation unit 5 estimates the position Xe of the evaluation point after receiving the instruction value of the instruction value calculation unit 9₁If the correction command is calculated by the correction command calculation unit 12, a delay occurs in the timing of inputting the correction command in the controller unit 6e due to the influence of the communication delay. Therefore, the simulation unit 5 and the correction command calculation unit 12 perform calculation in advance using the simulation command value calculation unit 90 that calculates the same position command Xc as the command value calculation unit 9. The correction command transmitting unit 31 transmits the correction command calculated in advance by the correction command calculating unit 12. The correction command transmission unit 31 may transmit the correction commands in a lump after the correction command calculation unit 12 performs all calculations of the correction commands for all the position commands Xc in advance. Further, the correction command transmitting unit 31 transmits the correction command at any time so that the simulated command value calculating unit 90 is activated at an earlier timing than the command value calculating unit 9 and the correction command is calculated by the correction command calculating unit 12, and the correction command reaches the correction command receiving unit 33 at an appropriate timing. Further, the instruction value calculation unit 9 and the simulation instruction value calculation unit 90 may be simultaneously activated, and the simulation unit 5 and the correction instruction calculation unit 12 may calculate the correction instruction at a processing cycle earlier than the actual time, so that the correction instruction transmission unit 31 may transmit the correction instruction at any time.

The operation flow of the servo control device 100f is the same as that of the servo control device 100 according to embodiment 1. The hardware configuration of the servo control device 100f is a transmitter that performs wireless communication or wired communication with the sensor signal transmitting unit 30 and the correction command transmitting unit 31. The sensor signal receiving unit 32 and the correction command receiving unit 33 are receivers that perform wireless communication or wired communication.

As described above, according to the present embodiment, even when the servo control devices 100f performing system recognition are distributed and installed at physically separated locations, control using a correction command can be performed at an appropriate timing without delay.

Embodiment 7.

In embodiments 1 to 6, an example of a transfer function calculation unit using a classical transfer function calculation algorithm having 1 Input and 1 output so-called siso (Single Input Single output) is described. In embodiment 7, a method of calculating a transfer function of a multiple Input multiple output (mimo) (Multi Input Multi output) by machine learning will be described.

Fig. 22 is a block diagram showing a configuration example of a servo control device 100g for performing system identification according to embodiment 7. A servo control device 100g according to embodiment 7 has a learning unit 13g in place of the first transfer function computing unit 4a and the second transfer function computing unit 4b, and a simulation unit 5g in place of the simulation unit 5, as compared to the servo control device 100 according to embodiment 1.

As the machine learning algorithm used by the learning unit 13g, any algorithm can be used. In the present embodiment, a case where deep learning, that is, dl (deep learning), is applied will be described as an example. Fig. 23 is a block diagram showing a configuration example of the learning unit 13g according to embodiment 7. The evaluation point acceleration Ae, the reference point acceleration Ar, and the detection position Xd are input to the learning unit 13 g. The evaluation point acceleration Ae is a state quantity of an evaluation point which is a mounting position of the evaluation point sensor 1 a. The reference point acceleration Ar is a state quantity of a reference point which is a mounting position of the reference point sensor 1 b. The detected position Xd is obtained by the controller unit 6a and is a state quantity of the motor 71 as an actuator. The learning unit 13g observes the state quantities of the evaluation points, the reference points, and the actuators acquired by the controller unit 6a as state variables, and learns from a training data set created based on the state variables. The learning unit 13g samples input data for each preset learning period, and uses the sampled data as a training data set for learning. The learning unit 13g learns a system model in which the detection position Xd is input and the state quantity of the evaluation point and the state quantity of the reference point are output, using the state quantity of the evaluation point, the state quantity of the reference point, and the detection position Xd, which is the state quantity of the actuator, in the training data set. The learning unit 13g includes a neural network unit 91g, a loss function calculation unit 92g, an optimizer unit 93g, and an ai (intellectual intelligence) model output unit 94 g.

The learning unit 13g learns the network structure parameter Mdl1 as a system model for predicting the learned evaluation point acceleration AeM and the learned reference point acceleration ArM at a predetermined time, that is, before a fixed time, using the evaluation point acceleration Ae, the reference point acceleration Ar, and the detection position Xd, which are training data sets. Specifically, the neural network unit 91g determines the network structure parameter Mdl1 by learning. Examples of the network configuration parameters Mdl1 include a weight parameter and an offset parameter. The neural network unit 91g receives the training data set as input, performs estimation processing, which is calculation of a neural network, using the network structure parameter Mdl1, and calculates the learning evaluation point acceleration AeM and the learning reference point acceleration ArM. The neural network unit 91g outputs the learning evaluation point acceleration AeM and the learning reference point acceleration ArM obtained by the calculation to the loss function calculation unit 92 g.

The loss function calculation unit 92g calculates the loss function Lf by using the actual evaluation point acceleration Ae and the reference point acceleration Ar, and the learned evaluation point acceleration AeM and the learned reference point acceleration ArM calculated by the neural network unit 91g as inputs. The loss function calculation unit 92g may use any loss function known in the art for mounting the loss function. As the simplest loss function, there is a square of the difference between the estimated value and the measured value. The loss function calculation unit 92g outputs the loss function Lf obtained by the calculation to the optimizer unit 93g and the AI model output unit 94 g.

The optimizer unit 93g updates the network structure parameter Mdl1 used in the next learning, using the loss function Lf obtained by the loss function calculation unit 92 g. The optimizer unit 93g calculates each parameter using a known technique such as back propagation, which is also called an error back propagation method.

The AI model output unit 94g takes the loss function Lf as an input, and compares the value of the loss function Lf with a predetermined threshold value. The AI model output unit 94g outputs the network configuration parameter Mdl1 to the simulation unit 5g when the value of the loss function Lf is equal to or less than a predetermined threshold value. The AI model output unit 94g may periodically output the network configuration parameters Mdl1 at a preset model update cycle.

The learning unit 13g may be configured to perform learning by combining the speed or acceleration of the motor 71 sampled by the controller unit 6a, the motor current flowing through the motor 71, and the like.

Fig. 24 is a block diagram showing a configuration example of the simulation unit 5g according to embodiment 7. The simulation unit 5g estimates the position Xe of the evaluation point corresponding to the temporal position command Xc input from the command value calculation unit 9, using the learning result of the learning unit 13g₁And a detected point estimated position Xd₁And the calculation is performed and output to the display 10. The simulation unit 5g according to embodiment 7 includes an AI model unit 58g, a 2-order integrator 95, and a 2-order integrator 96, instead of the reference point estimated position calculation unit 54 and the evaluation point estimated position calculation unit 56, in the simulation unit 5 according to embodiment 1. In the present embodiment, the simulation parameter setting unit 57 sets the network structure parameter Mdl1 learned by the learning unit 13g to the AI model unit 58g, and sets the stiffness value parameter Kdr to the driving reaction force estimating unit 55.

The AI model unit 58g is an artificial intelligence model unit having the same neural network structure as the neural network unit 91 g. The AI model unit 58g uses the evaluation point acceleration Ae, the reference point acceleration Ar, the network structure parameter Mdl1 set from the simulation parameter setting unit 57, and the detected point estimated position Xd calculated by the detected point estimated position calculation unit 53₁An estimation process, which is calculation of a neural network, is performed, and the estimated evaluation point acceleration Ae1 and the estimated reference point acceleration Ar1 are calculated. The AI model unit 58g outputs the estimated evaluation point acceleration Ae1 to the 2-order integrator 95, and outputs the estimated reference point acceleration Ar1 to the 2-order integrator 96.

The 2-order integrator 95 performs 2-order integration on the input estimated evaluation point acceleration Ae1, and converts the integrated value into an evaluation point estimated position Xe₁. The 2-order integrator 96 performs 2-order integration on the input estimated reference point acceleration Ar1, and converts the integrated value into a reference point estimated position Xr₁. The AI model unit 58g may have the functions of the 2-

step integrators

95 and 96. In the situationIn this case, the AI model unit 58g converts the estimated evaluation point acceleration Ae1 obtained by the calculation into the evaluation point estimated position Xe by 2-step integration₁I.e. estimating the position Xe for the evaluation points₁And (6) performing operation. Similarly, the AI model unit 58g converts the estimated reference point acceleration Ar1 obtained by the calculation into the reference point estimated position Xr by 2-order integration₁I.e. estimating the position Xr of the reference point₁And (6) performing operation.

The case where the learning unit 13g performs machine learning by deep learning has been described, but this is an example and is not limited thereto. The learning unit 13g may perform machine learning according to other known methods, for example, Q learning, genetic programming, functional logic programming, support vector machine, and the like.

The operation of the servo control device 100g will be described with reference to a flowchart. Fig. 25 is a flowchart showing the operation of the servo control device 100g according to embodiment 7. The operations in step S1 and step S2 are the same as those of the servo control device 100 according to embodiment 1 shown in the flowchart of fig. 8. In the servo control device 100g, the learning unit 13g learns the network structure parameter Mdl1 using the state quantities of the evaluation points, the state quantities of the reference points, and the detection position Xd (step S31). The simulation unit 5g estimates the position Xe for the evaluation point using the state quantities of the evaluation point, the state quantities of the reference point, the network structure parameter Mdl1, the position command Xc, and the stiffness value parameter Kdr₁And a detected point estimated position Xd₁An operation is performed (step S32). The hardware configuration of the servo control device 100g is realized by a processing circuit including the learning unit 13g and the simulation unit 5 g. The processing circuit may be a processor and a memory that execute a program stored in the memory, or may be dedicated hardware.

As described above, according to the present embodiment, the servo control device 100g that performs system recognition can obtain a multiple-input multiple-output system model without explicitly designing a model structure by a human being by applying a machine learning algorithm. Further, embodiment 1 has been described as an example, but the present invention is an example, and is not limited to this. This embodiment can also be applied to embodiments 2 to 6.

Embodiment 8.

In embodiment 7, a method of identifying a system model satisfying a model configuration of a simulation unit similar to those in embodiments 1 to 6 by machine learning is described. In embodiment 8, a method of recognizing models of simulation units having different structures by machine learning will be described.

Fig. 26 is a diagram showing a configuration example of a servo control device 100h for performing system identification according to embodiment 8. The servo control device 100h according to embodiment 8 includes a learning unit 13h and a simulation unit 5h in place of the learning unit 13g, the simulation unit 5g, and the rigidity value storage unit 8, as compared with the servo control device 100g according to embodiment 7.

Fig. 27 is a block diagram showing a configuration example of the learning unit 13h according to embodiment 8. The learning unit 13h includes a neural network unit 91h and a loss function calculation unit 92h instead of the neural network unit 91g and the loss function calculation unit 92g, as compared with the learning unit 13g of embodiment 7. The learning unit 13h learns a system model in which the state quantity of the evaluation point, the state quantity of the reference point, the detection position, which is the state quantity of the actuator, and the driving force of the actuator are used as an input in the training data set, and the state quantity of the evaluation point and the driving reaction force estimated value are output. The learning unit 13h includes a neural network unit 91h, a loss function calculation unit 92h, an optimizer unit 93h, and an AI model output unit 94 h.

The learning unit 13h uses the evaluation point acceleration Ae, the reference point acceleration Ar, the detected position Xd, and the motor current Im, which are training data sets, as the evaluation point acceleration Ae and the drive reaction force estimated value Tr₁The system model for predicting the learning evaluation point acceleration AeM and the learning drive reaction force TrM at a predetermined time, that is, before a fixed time, learns the network structure parameter Mdl 2. Specifically, the neural network unit 91h determines the network structure parameter Mdl2 by learning. Examples of the network configuration parameters Mdl2 include a weight parameter and an offset parameter. The neural network unit 91h performs estimation processing, which is calculation of a neural network, using the network structure parameter Mdl2, using the training data set as input, and performs learning of the evaluation point acceleration AeM and the learning drive reaction force TrMAnd (6) performing operation. The neural network unit 91h outputs the learning evaluation point acceleration AeM and the learning drive reaction force TrM obtained by the calculation to the loss function calculation unit 92 h.

The loss function calculation unit 92h calculates the loss function Lf using the actual evaluation point acceleration Ae, the reference point acceleration Ar, and the detection position Xd, as input, the motor current Im calculated by the controller unit 6a, and the learned evaluation point acceleration AeM and the learned driving reaction force TrM calculated by the neural network unit 91 h. At this time, the loss function calculating unit 92h calculates the driving reaction force calculation value Tr using the motor current Im and the detection position Xd by the following equation (2)₂。

[ formula 2 ]

Tr₂＝Im*Kt-Jm*Xd…(2)

In equation (2), Kt represents a torque constant for calculating the torque from the motor current Im, and Jm represents the inertia of the motor 71 alone. Thus, the loss function calculation unit 92h can use the learned evaluation point acceleration AeM, the learned drive reaction force Trm, the evaluation point acceleration Ae, and the executed drive reaction force calculation value Tr₂The loss function Lf is calculated.

The optimizer unit 93h updates the network structure parameter Mdl2 used in the next learning, using the loss function Lf obtained by the loss function calculation unit 92 h. The optimizer unit 93h calculates each parameter using a known technique such as back propagation, which is also called an error back propagation method.

The AI model output unit 94h takes the loss function Lf as an input, and compares the value of the loss function Lf with a predetermined threshold value. The AI model output unit 94h outputs the network configuration parameter Mdl2 to the simulation unit 5h when the value of the loss function Lf is equal to or less than a predetermined threshold value. The AI model output unit 94h may periodically output the network configuration parameters Mdl2 at a preset model update cycle with respect to the output timing.

Fig. 28 is a block diagram showing a configuration example of the simulation unit 5h according to embodiment 8. The simulation unit 5h according to embodiment 8 includes an AI model unit 58h in place of the driving reaction force estimation unit 55, the AI model unit 58g, and the 2 nd order integrator 96, as compared with the simulation unit 5g according to embodiment 7. In the present embodiment, the simulation parameter setting unit 57 sets the network structure parameters Mdl2 learned by the learning unit 13h to the AI model unit 58 h.

The AI model unit 58h is an artificial intelligence model unit having the same neural network structure as the neural network unit 91 h. The AI model unit 58h uses the network structure parameter Md2 input from the simulation parameter setting unit 57 and the estimated motor current Im calculated by the controller simulation unit 51₂And a detected point estimated position Xd calculated by the detected point estimated position calculating section 53₁And the estimated evaluation point acceleration Ae1 calculated by the AI model unit 58h before 1 sample, and the estimated evaluation point acceleration Ae1 and the driving reaction force estimated value Tr are subjected to the estimation processing that is the calculation of the neural network₁And (6) performing operation. The AI model unit 58h outputs the estimated evaluation point acceleration Ae1 to the 2-order integrator 95, and estimates the driving reaction force Tr₁And outputs the result to the addition/subtraction operator 61 e.

The 2-order integrator 95 performs 2-order integration on the input estimated evaluation point acceleration Ae1, and converts the integrated value into an evaluation point estimated position Xe₁. The AI model part 58h may have the function of the 2 nd order integrator 95. In this case, the AI model unit 58h converts the estimated evaluation point acceleration Ae1 obtained by the calculation into the evaluation point estimated position Xe by 2-step integration₁I.e. estimating the position Xe for the evaluation points₁And (6) performing operation.

The flowchart showing the operation of the servo control device 100h is the same as the servo control device 100g shown in fig. 25. However, the difference is that in step S32, the simulation unit 5h estimates the position Xe for the evaluation point₁And a detected point estimated position Xd₁For the calculation, the network configuration parameter Mdl2 and the position command Xc are used. The hardware configuration of the servo control device 100h is realized by a processing circuit in the learning unit 13h and the simulation unit 5 h. The processing circuit may be a processor and a memory that execute a program stored in the memory, or may be dedicated hardware.

As described above, according to the present embodiment, the servo control device 100h that performs system identification can perform estimation without using sensor data, actual actuator sample data, and the like when performing estimation by the AI model unit 58h using the network structure parameter Md2 obtained by machine learning. Thus, the servo control device 100h can set the simulation unit 5h at a physically separate place because the delay of communication can be eliminated when the estimation is performed by the AI model unit 58 h. Further, embodiment 1 has been described as an example, but the present invention is an example, and is not limited to this. This embodiment can also be applied to embodiments 2 to 6.

As described above, the servo control device according to the present invention calculates the transfer function from the detection point to the reference point and the transfer function from the reference point to the evaluation point using the signal of the acceleration sensor attached to the evaluation point as the control target, the acceleration calculated based on the detection position of the detection point to which the position detector is attached, and the signal of the acceleration sensor attached to the reference point set between the detection point and the evaluation point. The servo control device can be applied to a method of predicting the amount of the instantaneous trajectory error of the controlled object by using the calculated 2 transfer functions and the rigidity from the detection point to the reference point, and accurately measuring the trajectory error of the machine in an actual use state without detaching the jig or the tool.

The configuration described in the above embodiment is an example of the content of the present invention, and may be combined with other known techniques, and a part of the configuration may be omitted or modified without departing from the scope of the present invention.

Description of the reference numerals

1a evaluation point sensor, 1b reference point sensor, 1c, 1d gyro sensor, 2 rotation angle detector, 4a first transfer function arithmetic section, 4b second transfer function arithmetic section, 5g, 5h simulation section, 6a, 6b, 6e controller section, 7a, 7b, 7c machine section, 8 rigidity value storage section, 9 command value arithmetic section, 10 display, 11 control parameter changing section, 12 correction command arithmetic section, 13g, 13h learning section, 21 linear motion position detector, 30 sensor signal transmitting section, 31 correction command transmitting section, 32 sensor signal receiving section, 33 correction command receiving section, 51 controller simulation section, 53 detection point estimation position arithmetic section, 54 reference point estimation position arithmetic section, 55 drive reaction force estimation section, 56 evaluation point estimation position arithmetic section, 57 simulation parameter setting section, 58g, 58h AI model units, 61a, 61b, 61c, 61d, 61e, 61f, 61g add-subtract arithmetic units, 62 position controllers, 63 speed controllers, 64 current controllers, 65 speed arithmetic units, 66 acceleration arithmetic units, 67 multipliers, 68 filter units, 69a detection point position gain multiplying units, 69b position command gain multiplying units, 69c position correction gain multiplying units, 69d speed correction gain multiplying units, 69e current correction gain multiplying units, 70 torque presuming units, 71 motors, 72 mechanical structural members, 73 feed screws, 74 couplings, 75a, 75b support bearings, 76 tools, 77 tables, 78 work pieces, 79 nut blocks, 80 nuts, 81, 82 rotating tables, 83 spindle end faces, 90 simulated command value arithmetic units, 91g, 91h neural network units, 92g, 92h loss function arithmetic units, 93g and 93h optimizer units, 94g and 94h AI model output units, 95 and 962 order integrators, 99 numerical control machine tools, 100b, 100c, 100d, 100e, 100f, 100g and 100h servo control devices, and 101b X axis servo control devices.

Claims

1. A servo control device for feeding back a detected position indicating a position of a driven body detected by a position detector attached to a detection point, with respect to 1 or more actuators for driving a machine unit to which the driven body to be controlled is connected, and controlling the actuators by a controller unit so that the position of the driven body follows a position command for instructing a position calculated by a command value calculation unit,

the servo control device is characterized by comprising:

a first detection unit that detects a state quantity of an evaluation point set at the driven body or a position corresponding to the driven body;

a second detection unit that detects a state quantity of a reference point set in the mechanical unit between the evaluation point and the actuator;

a first transfer function calculation unit that calculates a first transfer function that is a frequency response characteristic from the reference point to the evaluation point, using the state quantity of the evaluation point and the state quantity of the reference point;

a second transfer function calculation unit that calculates a second transfer function that is a frequency response characteristic from the detection position to the reference point, using the state quantity of the reference point and the detection position; and

a first simulation unit that calculates an estimated position of the evaluation point using the first transfer function, the second transfer function, and a rigidity value parameter indicating a rigidity value between the detected position and the reference point,

the first simulation unit includes:

a controller simulation unit that generates a command torque estimation value for the actuator by simulating the controller unit using a detected point estimation position at which the behavior of the detected position is estimated as feedback;

a reference point estimated position calculation unit that calculates a reference point estimated position at which the behavior of the reference point is estimated, using the second transfer function and the detected point estimated position;

an evaluation point estimated position calculation unit that calculates an evaluation point estimated position at which the behavior of the evaluation point is estimated, using the first transfer function and the reference point estimated position;

a drive reaction force estimation unit that calculates a drive reaction force estimation value using the detected point estimation position, the reference point estimation position, and the rigidity value parameter;

a detected point estimated position calculation unit that calculates the detected point estimated position using an effective torque estimated value calculated from the drive reaction force estimated value and the command torque estimated value; and

and a simulator parameter setting unit that sets the first transfer function to the evaluation point estimated position calculating unit, sets the second transfer function to the reference point estimated position calculating unit, and sets the rigidity value parameter to the driving reaction force estimating unit.

2. A servo control device for feeding back a detected position indicating a position of a driven body detected by a position detector attached to a detection point, with respect to 1 or more actuators for driving a machine unit to which the driven body to be controlled is connected, and controlling the actuators by a controller unit so that the position of the driven body follows a position command for instructing a position calculated by a command value calculation unit,

the servo control device is characterized by comprising:

a learning unit that observes, as state variables, the state quantities of the evaluation points, the reference points, and the detected positions, which are the state quantities of the actuators acquired by the controller unit, and learns from a training data set created based on the state variables;

and a second simulation unit that calculates an estimated evaluation point position at which the behavior of the evaluation point is estimated and an estimated detection point position at which the behavior of the detection point is estimated, using the learning result of the learning unit.

3. Servo control device according to claim 1 or 2,

the first detection unit and the second detection unit are acceleration sensors.

4. Servo control device according to any of claims 1 to 3,

the position detector is a rotation angle detector attached to the actuator or a linear motion position detector attached to the mechanical unit.

5. Servo control device according to claim 1 or 2,

and a control parameter changing unit that changes a control parameter of the controller unit so that a motion accuracy of the driven body falls within a target value, using the estimated evaluation point position or the estimated evaluation point position and the estimated detection point position.

6. Servo control device according to claim 2,

the learning unit learns a system model in which the state quantity of the evaluation point, the state quantity of the reference point, and the detection position are used as input and the state quantity of the evaluation point and the state quantity of the reference point are used as output in a training data set.

7. Servo control device according to claim 2,

the learning unit learns a system model in which the state quantity of the evaluation point, the state quantity of the reference point, the detection position, and the driving force of the actuator are used as an input, and the state quantity of the evaluation point and the driving reaction force estimated value are output, in a training data set.

8. Servo control device according to claim 2 or 6,

the second simulation unit includes:

an artificial intelligence model unit that calculates the evaluation point estimation position and a reference point estimation position at which the behavior of the reference point is estimated, using the system model learned by the learning unit;

a drive reaction force estimation unit that calculates a drive reaction force estimation value using the detected point estimation position, the reference point estimation position, and a rigidity value parameter indicating a rigidity value between the detected position and the reference point;

and a simulator parameter setting unit that sets the network structure parameter learned by the learning unit in the artificial intelligence model unit, and sets the rigidity value parameter in the driving reaction force estimation unit.

9. Servo control device according to claim 2 or 7,

the second simulation unit includes:

an artificial intelligence model unit that calculates the estimated position of the evaluation point and the estimated value of the driving reaction force using the system model learned by the learning unit;

and a simulator parameter setting unit that sets the network structure parameter learned by the learning unit in the artificial intelligence model unit.

10. Servo control device according to claim 1,

the controller includes a correction command calculation unit that calculates at least 1 of a position correction command, a speed correction command, and a current correction command for the controller using the estimated evaluation point position or the estimated evaluation point position and the estimated detection point position, and outputs the calculated result to the controller.

11. The servo control apparatus according to claim 10, comprising:

a first transmission unit that transmits the state quantity of the evaluation point, the state quantity of the reference point, and the detection position;

a first receiving unit that receives the state quantity of the evaluation point, the state quantity of the reference point, and the detection position, outputs the state quantity of the evaluation point and the state quantity of the reference point to the first transfer function calculating unit, and outputs the state quantity of the reference point and the detection position to the second transfer function calculating unit;

a second transmitting unit that transmits at least 1 of the position correction command, the speed correction command, and the current correction command; and

and a second receiving unit that receives at least 1 of the position correction command, the speed correction command, and the current correction command and outputs the received command to the controller unit.