JP2009175917A - Controlled parameter adjustment method and controlled parameter adjustment program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To directly decide a controlled parameter from a set of test data. <P>SOLUTION: When adjusting a control parameter x which decides a transfer function C(x) of a control device 12 which controls a controlled object 11 by giving a manipulated variable u(t) with an open loop so that a response of the controlled variable y(t) of the controlled object 11 to a desired value r(t) may approach a reference model M, a test output signal string y<SB>1</SB>(t) which is a time change of a controlled variable is extracted by giving a test input signal string u<SB>1</SB>(t) changing with time of day to the controlled object, and the control parameter x is decided so that an evaluation function J which is a transformed error ¾ ¾Mr(t)-PC(x)r(t)¾¾ may become minimum. When noting to y<SB>1</SB>(t)=Pu<SB>1</SB>(t), the evaluation function J is represented by a transfer function C(x) of the control device 12 C represented by an unknown control parameter x, the known transfer function M, the test input signal string u<SB>1</SB>(t) and the test output signal string y<SB>1</SB>(t) and becomes J=¾ ¾Mu<SB>1</SB>(t)-C(x)y<SB>1</SB>(t)¾¾. Note that ¾¾ is a norm and it is a sign showing the length of geometric vector. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a control parameter adjustment method and a control parameter adjustment program for adjusting a control parameter of a control device that controls a control target.

  When a control target is controlled by a control device, it is necessary to adjust control parameters in accordance with the characteristics of the control target. In general, the control parameter is calculated from response data to be controlled.

  Control parameter adjustment methods can be broadly divided into two methods: a method in which a test is performed once to determine a control parameter, and a method in which a test is repeatedly performed to update a parameter (see, for example, Non-Patent Document 1).

  As one of the methods that does not repeat the test, Patent Document 1 discloses a method of obtaining a mathematical model of a plant from the input and output of a controlled object, and obtaining control parameters from the mathematical model by model matching or Ziegler-Nichols method. Is disclosed.

  In this method, a model to be controlled is created from input / output signals to be controlled. Modeling includes a parametric model such as a transfer function to be controlled, and a method of modeling feature quantities such as dead time, rise time, attenuation rate, and overshoot amount. In the case of modeling, it is necessary that the input / output data to be controlled contains a large amount of information, so that the test signal is generally changed.

  Control parameters are obtained from information about the modeled control target (transfer function parameters, feature quantities, etc.). For this control parameter calculation, model matching or Ziegler-Nichols method is used. Fuzzy inference can also be used as a control parameter calculation method.

Patent Document 2 discloses a method in which a neural network is applied as a method for updating a control parameter by repeatedly performing a test.
Japanese Patent Laid-Open No. 2-190902 Japanese Patent No. 2862308 Nobuhide Suda Author Representative, “System Control Information Library 6 PID Control”, Asakura Shoten, July 20, 1992

  The method for obtaining a parameter from a model or feature quantity to be controlled without repeating the test is a two-stage method in which the model or feature quantity to be controlled is first obtained and then the control parameter is determined based on the design side. . In general, a lot of labor and data are required to extract models and feature quantities suitable for control design. Furthermore, an error occurs in the model and the feature amount due to noise and nonlinearity of the control target. For this reason, it is not always clear whether the correct control parameter is determined from the obtained model or feature quantity. In the method using fuzzy reasoning, the rules for calculating the parameters are arbitrary and the objectivity is poor. For this reason, it takes time and labor to build rules.

  Further, in the method of updating the control parameter by repeating the test, it is necessary to repeatedly perform the test for determining the parameter, so that a lot of time and a lot of test data are required.

  Therefore, an object of the present invention is to enable control parameters to be determined directly from a set of test data.

  In order to achieve the above-mentioned object, the present invention provides a control parameter for determining a transfer function of a control device for controlling a control target by giving an operation amount to the control target in an open loop when the target value is input, In the control parameter adjustment method for adjusting the response of the control amount of the control object to a reference model, a test output signal that is a time change of the control amount by giving a test input signal sequence that changes with time to the control object A sequence of sampling, and a data sequence when the test output signal sequence is given to the transfer function of the control device is subtracted from a data sequence when the test input signal sequence is given to the transfer function of the reference model. And a control parameter determination step for determining the control parameter so as to minimize the evaluation function.

  Further, the present invention provides a control parameter for determining a transfer function of a control device that receives a target value and feedback from the control target and gives an operation amount to control the control target, and controls the control target with respect to the target value. In a control parameter adjustment method for adjusting a response of a quantity so as to approach a reference model, a test input signal sequence that changes with time is given to the control target, and a test output signal sequence that is a time change of the control amount is collected; From the data sequence when the test input signal sequence is given to the transfer function of the reference model, the data sequence when the test output signal sequence is given to the transfer function of the control device and the transfer function of the reference model A control parameter for determining the control parameter so as to minimize the evaluation function obtained by subtracting the data series when the test output signal sequence is given. And having a meter determining step.

  Further, the present invention provides a control parameter for determining a transfer function of a control device that controls a control target affected by the disturbance by inputting a disturbance and providing an operation amount, and a control parameter of the control target of the control target with respect to the disturbance. In the control parameter adjustment method for adjusting the response so that the response approaches a reference model, the control amount is changed with time by giving a test input signal that changes with time by simulating the disturbance without giving the operation amount to the control target. Sampling a disturbance response signal sequence, and removing the influence of the disturbance on the controlled object, providing the test input signal sequence, and sampling a test output signal sequence that is a time change of the control amount; Subtracting the disturbance response signal sequence from the data sequence when the test input signal sequence is given to the transfer function of the reference model, and outputting the test output to the transfer function of the control device And having a control parameter determining step of determining the control parameters so as to minimize the evaluation function obtained by adding the data sequence when given a No. column.

  Further, the present invention provides a control parameter for determining a transfer function of a control device for controlling a control target by giving an operation amount to the control target in an open loop by inputting the target value, and controlling the control target with respect to the target value. In a control parameter adjustment program for adjusting a response of a quantity so as to approach a reference model, a test input signal sequence that changes with time is given to the control target, and a test output signal sequence that is a time change of the control quantity is collected. And an evaluation function obtained by subtracting the data sequence when the test output signal sequence is given to the transfer function of the control device from the data sequence when the test input signal sequence is given to the transfer function of the reference model. And a control parameter determining function for determining the control parameter so as to be minimized.

  Further, the present invention provides a control parameter for determining a transfer function of a control device that receives a target value and feedback from the control target and gives an operation amount to control the control target, and controls the control target with respect to the target value. In a control parameter adjustment program for adjusting a response of a quantity so as to approach a reference model, a test input signal sequence that changes with time is given to the control target, and a test output signal sequence that is a time change of the control quantity is collected. And a data sequence when the test output signal sequence is given to the transfer function of the control device and a transfer of the reference model from a data sequence when the test input signal sequence is given to the transfer function of the reference model The control parameter is set to minimize the evaluation function obtained by subtracting the data series when the test output signal sequence is given to the function. Characterized in that to achieve a control parameter determination function of determining over data, the.

  Further, the present invention provides a control parameter for determining a transfer function of a control device that controls a control target affected by the disturbance by inputting a disturbance and providing an operation amount, and a control parameter of the control target of the control target with respect to the disturbance. In the control parameter adjustment program for adjusting the response so that the response approaches a reference model, the control amount is provided by giving a test input signal that changes with time to simulate the disturbance without giving the operation amount to the control target. The function of collecting a disturbance response signal sequence that is a time change of the above, and the influence of the disturbance on the control target is eliminated, and the test output signal sequence that is a time change of the control amount is obtained by giving the test input signal sequence And subtracting the disturbance response signal sequence from the data sequence when the test input signal sequence is given to the transfer function of the reference model, Characterized in that to realize a control parameter determination function for determining the control parameters so as to minimize the evaluation function obtained by adding the data sequence when given the test output signal sequence to function.

  According to the present invention, control parameters can be determined directly from a set of test data.

  An embodiment of a control parameter adjustment method according to the present invention will be described with reference to the drawings. In addition, the same code | symbol is attached | subjected to the same or similar structure, and the overlapping description is abbreviate | omitted.

[First Embodiment]
FIG. 1 is a block diagram of a control parameter adjustment system in a first embodiment of a control parameter adjustment method according to the present invention.

  In the present embodiment, the control parameter of the control device 12 that controls the control target 11 is adjusted in a so-called open loop without feedback. It is assumed that the control target 11 is represented by one process 31. The control parameter is adjusted by the control parameter adjustment system 13.

An input value r (t) is input to the control device 12. This input value r (t) is, for example, a target value for the control amount. The control device 12 calculates the operation amount u (t) of the controlled object 11 based on the input value r (t). The transfer function of the control device 12 is represented as C (x). Here, x is a control parameter. That is, in the control device 12,
u (t) = C (x) r (t)
Perform the operation.

The control device 12 controls the control object 11 by inputting the operation amount u (t) to the control object 11. When the transfer function of the control object 11 is P, the control amount y (t) output from the control object is
y (t) = Pu (t)
It is expressed.

  The control parameter adjustment system 13 includes a reference model M that represents a transfer function of an ideal response of the control amount y (t) output from the control target 11 with respect to the input to the control device 12. The control parameter adjustment system 13 adjusts the control parameter x of the control device 12 so that the response of the control amount y (t) output from the control target 11 to the input to the control device 12 approaches this reference model M.

The ideal response of the controlled object when the input value r (t) is input to the control device 12 is expressed as Mr (t). On the other hand, the actual response of the control object 11 at this time is
y (t) = Pu (t) = PC (x) r (t)
It is expressed. Therefore, the response of the control amount y (t) output from the control object 11 to the input to the control device 12 approaches this reference model M.
e = ‖Mr (t) −PC (x) r (t) ‖
This means that the error e represented by Here, ‖z (t) ‖ represents the norm of the signal sequence z (t).

In order to adjust the control parameter x, first, the test input signal sequence u ₁ (t) is input to the control target 11. The test input signal sequence u ₁ (t) is generated by the control parameter adjustment system 13. At this time, the control parameter adjustment system 13 collects a test output signal sequence y ₁ (t) that is a time change of the control amount output from the control target 11. When this test output signal sequence y ₁ (t) uses the transfer function P to be controlled,
y ₁ (t) = Pu ₁ (t)
It is expressed.

When attention is paid to this equation, when the input value r (t) to the control device 12 is the test input signal sequence u ₁ (t), the error e is
e = ‖Mr (t) −PC (x) r (t) ‖
= ‖Mu ₁ (t) −PC (x) u ₁ (t) ‖
= ‖Mu ₁ (t) -C (x) Pu ₁ (t) ‖
= ‖Mu ₁ (t) -C (x) y ₁ (t) ‖
It becomes. The error e is a control index that quantitatively indicates how much the control of the control target 11 by the control device 12 differs from the ideal reference model M.

The error e is represented by C (x) y ₁ (t) and Mu ₁ (t). C (x) y ₁ (t) is a response when the test output signal sequence y ₁ (t) is input to the transfer function C (x) of the control device 12. Further, Mu ₁ (t) is a response when the test input signal sequence u ₁ (t) is input to the reference model M of the control target 11. That is, bringing the response of the control amount y (t) output from the control object 11 to the input to the control device 12 closer to the reference model M is
J = ‖Mu ₁ (t) −C (x) y ₁ (t) ‖
This results in minimizing the evaluation function J expressed by That is, the evaluation function J includes the transfer function C (x) of the control device 12 represented by the unknown control parameter x, the known transfer function M, the test input signal sequence u ₁ (t), and the test output signal sequence y. ₁ (t). Therefore, the control parameter x can be determined so as to minimize the evaluation function J. Here, the evaluation function J is tested from the data series Mu ₁ (t) when the test input signal sequence u ₁ (t) is given to the transfer function M of the reference model to the transfer function C (x) of the control device 12. The data series C (x) y ₁ (t) when the output signal sequence y ₁ (t) is given is subtracted.

If the test input signal sequence u ₁ (t) and the corresponding test output signal sequence y ₁ (t) are N pieces of time series data, the evaluation function J is expressed by the following equation.

When the control device 12 is a PID control device, the transfer function C (x) of the control device 12 is
C (x) = x ₁ + x ₂ / (1-z ⁻¹ ) + (1-z ⁻¹ ) x ₃
It is expressed. _{Here, x 1, x 2, x} 3 is the control parameter. Since these control parameters x ₁ , x ₂ , and x ₃ are linear, the control parameter x that minimizes the evaluation function J can be obtained by, for example, the least square method.

  The sampling frequencies of the test input signal sequence and the test output signal sequence are made small enough to express the response of the controlled object 11 sufficiently. The sampling time needs to be several times the minimum frequency of the response of the control object 11. For example, when there are three control parameters, data of about 500 points or more is necessary.

  As described above, by using the control parameter adjustment method of the present embodiment, a control parameter that minimizes an evaluation function that quantitatively represents a control index can be obtained even in the case of open loop control. In addition, the control parameter can be calculated directly from a set of test data. For this reason, it is not necessary to repeat the test for data collection, and the time and cost required for adjusting the control parameters can be reduced.

  The control parameter adjustment method of the present embodiment can also be realized by a program that causes one or more computers to operate the control parameter adjustment system 13, for example.

[Second Embodiment]
FIG. 2 is a block diagram of a control parameter adjustment system in the second embodiment of the control parameter adjustment method according to the present invention.

  In the present embodiment, the control parameter of the control device 12 that controls the control target 11 by controlling the control amount y (t) of the control target 11 is adjusted. This control system includes a subtractor 21, which calculates a deviation between the target value r (t) and the controlled variable y (t) and outputs it to the control device 12. The control device 12 calculates the operation amount u (t) of the controlled object 11 based on this deviation.

The ideal response of the controlled object when the target value r (t) is input to the subtractor 21 is expressed as Mr (t). On the other hand, the actual response of the control object 11 at this time is
y (t) = (PC (x) / (1 + PC (x))) r (t)
It is expressed. Therefore, the response of the control amount y (t) output from the control object 11 to the input to the control device 12 approaches this reference model M.
e = ‖Mr (t) − (PC (x) / (1 + PC (x))) r (t) ‖
This means that the error e represented by Here, an evaluation function J represented by the following equation is introduced.

J = ‖ (1 + PC (x)) Mr (t) −PC (x) r (t) ‖
Using this J, e becomes as follows.
e = J / (1 + PC (x))
If the process represented by 1 / (1 + PC (x)) is stable, the gain of 1 / (1 + PC (x)) has a maximum value K. Therefore, the error e is
e = J / (1 + PC (x)) ≦ KJ
It is expressed. That is, when J is minimized, e is minimized, so the problem of minimizing e results in the problem of minimizing J.

Therefore, the test input signal sequence u ₁ (t) is input to the control target 11, and the test output signal sequence y ₁ (t) that is a time change of the control amount output from the control target 11 is collected. When y ₁ (t) = Pu ₁ (t) is used, the evaluation function J when the input value r (t) to the subtractor 21 is the test input signal sequence u ₁ (t) is expressed as follows: Is done.

J = ‖ (1 + PC (x)) Mr (t) −PC (x) r (t) ‖
= ‖Mr (t) −PC (x) (1-M) r (t) ‖
= ‖Mu ₁ (t) -C (x) (1-M) Pu ₁ (t) ‖
= ‖Mu ₁ (t) -C (x) (1-M) y ₁ (t) ‖
That is, the evaluation function includes the transfer function C (x) of the control device 12 represented by the unknown control parameter x, the known transfer function M, the test input signal sequence u ₁ (t), and the test output signal sequence y ₁ ( t). Therefore, the control parameter x can be determined so as to minimize the evaluation function J.

Here, the evaluation function J refers to this data sequence from the data sequence C (x) y ₁ (t) when the test output signal sequence y ₁ (t) is given to the transfer function C (x) of the control device 12. C (x) (1-M) y ₁ (t) obtained by subtracting the data series MC (x) y ₁ (t) given to the model transfer function is used as the test input signal for the transfer function M of the reference model. Subtracted from the data series Mu ₁ (t) given the column u ₁ (t).

If the test input signal sequence u ₁ (t) and the corresponding test output signal sequence y ₁ (t) are N pieces of time series data, the evaluation function J is expressed by the following equation.

  If the control parameter x is linear, the control parameter x that minimizes the evaluation function J can be obtained by, for example, the least square method.

  In this way, by using the control parameter adjustment method of the present embodiment, it is possible to obtain a control parameter that minimizes an evaluation function that quantitatively represents a control index even in the case of feedback control. In addition, the control parameter can be calculated directly from a set of test data. For this reason, it is not necessary to repeat the test for data collection, and the time and cost required for adjusting the control parameters can be reduced.

[Third Embodiment]
FIG. 3 is a block diagram of a control parameter adjustment system in the third embodiment of the control parameter adjustment method according to the present invention.

In the present embodiment, the control parameter of the control device 12 that controls the control object 11 that is affected by the disturbance d (t) is adjusted. In general, the control target 11 affected by the disturbance d (t) includes a first process 32 that changes the control amount y (t) by the operation amount u (t) from the control device 12, and the disturbance d (t). Can be considered to be expressed by the second process 33 that changes the control amount y (t). Here, the transfer function of the first process 32 to _{P 1.} The transfer function of the second process 33 and _{P 2.}

The control device 12 receives the disturbance d (t) and calculates the operation amount u (t) of the controlled object 11 based on the disturbance d (t). That is, the control device 12 performs disturbance feedforward control that generates an operation amount u (t) from the disturbance d (t).
u (t) = C (x) d (t)
Perform the operation.

The control device 12 controls the control object 11 by inputting the operation amount u (t) to the control object 11. The output from the first process 32 is added to the output from the second process by the adder 22. For this reason, the controlled variable y (t) output from the controlled object is
y (t) = P ₂ d (t) + P ₁ u (t)
It is expressed.

  The control parameter adjustment system 13 includes a reference model M that represents a transfer function of an ideal response of the control amount y (t) output from the control target 11 with respect to the input to the control device 12. Here, the input to the control device 12 is a disturbance d (t). The control parameter adjustment system 13 adjusts the control parameter x of the control device 12 so that the response of the control amount y (t) output from the control target 11 to the input to the control device 12 approaches this reference model M.

The ideal response of the controlled object when the disturbance d (t) is input to the control device 12 is expressed as Md (t). On the other hand, the actual response of the control object 11 at this time is
y (t) = P ₂ d (t) + P ₁ u (t)
= P ₂ d (t) + P ₁ C (x) d (t)
It is expressed. Therefore, the response of the control amount y (t) output from the control object 11 to the input to the control device 12 approaches this reference model M.
e = ‖Md (t) − (P ₂ d (t) + P ₁ C (x) d (t)) ‖
This means that the error e represented by

In order to adjust the control parameter x, first, the operation amount u (t) = 0 is set, and the test input signal sequence d ₁ (t) is input to the controlled object 11 as a disturbance. The control parameter adjustment system 13 generates the test input signal sequence d ₁ (t). At this time, the control parameter adjustment system 13 collects a disturbance response signal sequence w (t) that is a time change of the control amount output from the control target 11.

Further, the test input signal sequence d ₁ (t) is given to the controlled object 11 instead of the manipulated variable u (t) in a state where the disturbance d (t) is not applied. At this time, the control parameter adjustment system 13 collects a test output signal sequence y ₁ (t) that is a time change of the control amount output from the control target 11.

In this way, a set of test data of the test input signal sequence d ₁ (t), the disturbance response signal sequence w (t), and the test output signal sequence y ₁ (t) is collected. The disturbance response signal sequence w (t) is
w (t) = P ₂ d ₁ (t)
It is expressed. The test output signal sequence y ₁ (t) is
y ₁ (t) = P ₁ d ₁ (t)
It is expressed.

When attention is paid to these equations, when the disturbance d (t) is the test input signal sequence d ₁ (t), the error e is expressed by the following equation.

e = ‖Md ₁ (t) − (P ₂ d ₁ (t) + P ₁ C (x) d ₁ (t)) ‖
= ‖Md ₁ (t) −P ₂ d ₁ (t) + C (x) P ₁ d ₁ (t) ‖
_{= ‖Md 1 (t) -w (} t) + C (x) y 1 (t) ‖
The error e is a control index that quantitatively indicates how much the control of the control target 11 by the control device 12 differs from the ideal reference model M.

That is, bringing the response of the control amount y (t) output from the control object 11 to the input to the control device 12 closer to the reference model M is
_{J = ‖Md 1 (t) -w} (t) + C (x) y 1 (t) ‖
This results in minimizing the evaluation function J expressed by The evaluation function J includes a transfer function C (x) of the control device 12 represented by an unknown control parameter x, a known transfer function M, a test input signal sequence d ₁ (t), and a disturbance response signal sequence w (t ) And a test output signal sequence y ₁ (t). Therefore, the control parameter x can be determined so as to minimize the evaluation function J.

Here, the evaluation function J is obtained by subtracting the disturbance response signal sequence w (t) from the data sequence Md ₁ (t) when the test input signal sequence d ₁ (t) is given to the transfer function M of the reference model. The data series C (x) y ₁ (t) when the test output signal sequence y ₁ (t) is given is added to the transfer function C (x) of the device 12.

If the test input signal sequence d ₁ (t), the corresponding disturbance response signal sequence w (t) and the test output signal sequence y ₁ (t) are N pieces of time series data, the evaluation function J is It is expressed by the following formula.

  If the control parameter x is linear, the control parameter x that minimizes the evaluation function J can be obtained by, for example, the least square method.

  Thus, by using the control parameter adjustment method of the present embodiment, a control parameter that minimizes an evaluation function that quantitatively represents a control index can be obtained even in the case of disturbance feedforward control. it can. In addition, the control parameter can be calculated directly from a set of test data. For this reason, it is not necessary to repeat the test for data collection, and the time and cost required for adjusting the control parameters can be reduced.

[Fourth Embodiment]
FIG. 4 is a block diagram of a control parameter adjustment system in the fourth embodiment of the control parameter adjustment method according to the present invention.

  The present embodiment is a control parameter adjustment method when a feedback control system is added to the controlled object of the third embodiment. This feedback control system includes a subtractor 21 that obtains a deviation between the target value r (t) and the controlled variable y (t), and a feedback controller 34 that multiplies the deviation by a gain K. The output of the feedback controller 34 is added to the manipulated variable u (t) by the adder 22 and given to the first process 32.

Assuming that the target value r (t) is constant, the response of the control amount y (t) output from the control target 11 to the input to the control device 12 is brought close to the reference model M.
J = ‖Md (t)
− (P ₂ d (t) + C (x) (P ₁ / (1 + P ₁ K)) d (t)) ‖
This results in minimizing the evaluation function J expressed by

In order to adjust the control parameter x, first, the operation amount u (t) = 0 is set, and the test input signal sequence d ₁ (t) is input to the controlled object 11 as a disturbance. At this time, the control parameter adjustment system 13 collects a disturbance response signal sequence w (t) that is a time change of the control amount output from the control target 11.

Further, the test input signal sequence d ₁ (t) is given to the controlled object 11 instead of the manipulated variable u (t) in the state where the disturbance d (t) is not applied. At this time, the control parameter adjustment system 13 collects a test output signal sequence y ₁ (t) that is a time change of the control amount output from the control target 11.

The disturbance response signal sequence w (t) is
w (t) = P ₂ d ₁ (t)
It is expressed. The test output signal sequence y ₁ (t) is
y ₁ (t) = (P ₁ / (1 + P ₁ K)) d ₁ (t)
It is expressed.

When attention is paid to these equations, when the disturbance d (t) is the test input signal sequence d ₁ (t), the evaluation function J is expressed by the following equation.

J = ‖Md ₁ (t)
− (P ₂ d ₁ (t) + C (x) (P ₁ / (1 + P ₁ K)) d ₁ (t)) ‖
_{= ‖Md 1 (t) -w (} t) -C (x) y 1 (t) ‖
The evaluation function J is a control index that quantitatively indicates how much the control of the controlled object 11 by the control device 12 differs from the ideal reference model M. The evaluation function J includes a transfer function C (x) of the control device 12 represented by an unknown control parameter x, a known transfer function M, a test input signal sequence d ₁ (t), and a disturbance response signal sequence w (t ) And a test output signal sequence y ₁ (t). Here, the evaluation function J is obtained by subtracting the disturbance response signal sequence w (t) from the data sequence Md ₁ (t) when the test input signal sequence d ₁ (t) is given to the transfer function M of the reference model. The data series C (x) y ₁ (t) when the test output signal sequence y ₁ (t) is given is added to the transfer function C (x) of the device 12.

  If the control parameter x is linear, the control parameter x that minimizes the evaluation function J can be obtained by, for example, the least square method.

  In this way, by using the control parameter adjustment method of the present embodiment, control that minimizes an evaluation function that quantitatively represents a control index even when the control target has a feedback control system. Parameters can be determined. In addition, the control parameter can be calculated directly from a set of test data. For this reason, it is not necessary to repeat the test for data collection, and the time and cost required for adjusting the control parameters can be reduced.

[Other embodiments]
The above-described embodiments are merely examples, and the present invention is not limited to these. Moreover, you may implement combining the characteristic of each embodiment.

1 is a block diagram of a control parameter adjustment system in a first embodiment of a control parameter adjustment method according to the present invention. It is a block diagram of the control parameter adjustment system in 2nd Embodiment of the control parameter adjustment method which concerns on this invention. It is a block diagram of the control parameter adjustment system in 3rd Embodiment of the control parameter adjustment method which concerns on this invention. It is a block diagram of the control parameter adjustment system in 4th Embodiment of the control parameter adjustment method which concerns on this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 11 ... Control object, 12 ... Control apparatus, 13 ... Control parameter adjustment system, 21 ... Subtractor, 22 ... Adder, 31, 32, 33 ... Process, 34 ... Feedback controller

Claims (8)

A control parameter that determines the transfer function of the control device that controls the control target by giving an operation amount to the control target in an open loop by inputting the target value, and the response of the control amount of the control target to the target value is a reference model In the control parameter adjustment method of adjusting so as to approach
Providing a test input signal sequence that changes with time to the control target, and taking a test output signal sequence that is a time change of the control amount;
The evaluation function obtained by subtracting the data sequence when the test output signal sequence is given to the transfer function of the control device from the data sequence when the test input signal sequence is given to the transfer function of the reference model is minimized. A control parameter determining step for determining the control parameter;
A control parameter adjustment method comprising: A control parameter for determining a transfer function of a control device that receives a target value and feedback from the control target and gives an operation amount to control the control target, and a response of the control amount of the control target to the target value is a reference model In the control parameter adjustment method of adjusting so as to approach
Providing a test input signal sequence that changes with time to the control target, and taking a test output signal sequence that is a time change of the control amount;
From the data sequence when the test input signal sequence is given to the transfer function of the reference model, the test to the data sequence when the test output signal sequence is given to the transfer function of the control device and the transfer function of the reference model A control parameter determination step for determining the control parameter so as to minimize an evaluation function obtained by subtracting a data series when an output signal sequence is given;
A control parameter adjustment method comprising: The control parameter that determines the transfer function of the control device that controls the control target affected by the disturbance by inputting the disturbance and giving the manipulated value, the response of the control amount of the control target to the disturbance approaches the reference model In the control parameter adjustment method to adjust as follows:
Collecting a disturbance response signal sequence that is a time change of the control amount by giving a test input signal that changes with time by simulating the disturbance without giving the operation amount to the control target;
Removing the influence of the disturbance on the controlled object, providing the test input signal sequence, and collecting a test output signal sequence that is a time change of the control amount;
By subtracting the disturbance response signal sequence from the data sequence when the test input signal sequence is given to the transfer function of the reference model, the data sequence when the test output signal sequence is given to the transfer function of the control device is A control parameter determining step for determining the control parameter so as to minimize the added evaluation function;
A control parameter adjustment method comprising:   4. The control parameter according to claim 1, wherein the control parameter is linear, and the control parameter determination step is a step of determining the control parameter by a least square method. Adjustment method.   5. The control parameter adjusting method according to claim 4, wherein the control device is a PID controller, and the control parameter is a gain of each of a proportional element, an integral element, and a derivative element of the PID controller. A control parameter for determining a transfer function of a control device that controls the control target by giving an operation amount to the control target in an open loop by inputting the target value, and a response of the control amount of the control target to the target value is a reference model In the control parameter adjustment program that adjusts to approach
A function of taking a test input signal sequence that changes with time by giving a test input signal sequence that changes with time to the control target;
An evaluation function obtained by subtracting the data sequence when the test output signal sequence is given to the transfer function of the control device from the data sequence when the test input signal sequence is given to the transfer function of the reference model is minimized. A control parameter determination function for determining the control parameter;
A control parameter adjustment program characterized by realizing the above. A control parameter for determining a transfer function of a control device that receives a target value and feedback from the control target and gives an operation amount to control the control target, and a response of the control amount of the control target to the target value is a reference model In the control parameter adjustment program that adjusts to approach
A function of taking a test input signal sequence that changes with time by giving a test input signal sequence that changes with time to the control target;
From the data sequence when the test input signal sequence is given to the transfer function of the reference model, the test to the data sequence when the test output signal sequence is given to the transfer function of the control device and the transfer function of the reference model A control parameter determination function for determining the control parameter so as to minimize an evaluation function obtained by subtracting a data series when an output signal sequence is given;
A control parameter adjustment program characterized by realizing the above. The control parameter that determines the transfer function of the control device that controls the control target affected by the disturbance by inputting the disturbance and giving the manipulated value, the response of the control amount of the control target to the disturbance approaches the reference model In the control parameter adjustment program for adjusting
A function of sampling a disturbance response signal sequence that is a time change of the control amount by giving a test input signal that changes with time by simulating the disturbance without giving the operation amount to the control target;
A function of eliminating the influence of the disturbance on the controlled object, and providing the test input signal sequence to collect a test output signal sequence that is a time change of the control amount;
By subtracting the disturbance response signal sequence from the data sequence when the test input signal sequence is given to the transfer function of the reference model, the data sequence when the test output signal sequence is given to the transfer function of the control device is A control parameter determination function for determining the control parameter so as to minimize the added evaluation function;
A control parameter adjustment program characterized by realizing the above.

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