CN112000016A - Multi-objective optimization method for motor controller parameters - Google Patents
Multi-objective optimization method for motor controller parameters Download PDFInfo
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- CN112000016A CN112000016A CN202010927601.2A CN202010927601A CN112000016A CN 112000016 A CN112000016 A CN 112000016A CN 202010927601 A CN202010927601 A CN 202010927601A CN 112000016 A CN112000016 A CN 112000016A
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- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
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Abstract
The invention relates to a multi-objective optimization method for motor controller parameters, and belongs to the field of motor control. On the basis of establishing a motor control system, the invention takes system controller parameters as optimization variables, and the optimization target simultaneously comprises a static performance index and a dynamic performance index: ITAE criterion, phase shift amount under the tracking of sine signals, and overshoot amount under step signals; and optimizing the control parameters by adopting a multi-objective optimization algorithm, solving Pareto frontier, and finally selecting final control parameters by combining the controller parameters and a correlation coefficient matrix of an optimization target. Compared with the traditional single-target optimization method, the method has the advantages that multiple performances of the control system are considered; compared with the traditional multi-target optimization method, the tracking performance under the sine input signal, the performance of the control system under the step input signal and the correlation coefficient matrix of the controller parameter and the optimization target are considered at the same time, and the efficiency and the applicability of parameter adjustment of the motor controller are improved.
Description
Technical Field
The invention relates to the technical field of motor control, in particular to a method for optimizing parameters of a motor controller.
Background
With the development of modern science and technology, motors are receiving much attention and have been widely applied to the fields of military affairs, aerospace, robot motion, modern machine tools and the like. Whether a linear motor or a rotary motor, controller design is critical to determining the performance of the motor system.
In the design of the motor controller, whether traditional PID control or modern nonlinear control is applied to motor control, such as sliding mode variable structure control, adaptive control, fuzzy sliding mode control and the like, the parameters of the controller need to be adjusted. Conventional parameter adjustments rely on the developer's experience and can only be made to the controller parameters one by one. In order to improve the efficiency of parameter adjustment, offline or online optimization methods for controller parameters have been used in large numbers. Because the offline controller parameter optimization does not occupy the actual controller resource, the cost of the product is reduced, and the method is more widely applied compared with the online controller parameter optimization. However, most of the existing offline controller parameter optimization methods are directed at a single optimization target, or only directed at multiple optimization targets under a certain control input, so that the controller parameters obtained by optimization have low adaptability and are difficult to meet the requirements of numerous working conditions of the motor.
The invention discloses a multi-target optimization method for motor controller parameters, which adopts an ITAE (iterative optimization) rule, a phase shift amount under the tracking of a sine signal and an overshoot amount under a step signal as optimization targets, adopts a multi-target optimization algorithm to optimize control parameters, solves Pareto frontier, and finally selects final control parameters by combining a correlation coefficient matrix of the controller parameters and the optimization targets. Compared with the traditional multi-target optimization method, the method simultaneously considers the track tracking performance under the sine input signal, the control system performance under the step input signal and the correlation coefficient matrix of the controller parameter and the optimization target, and improves the efficiency and the applicability of parameter adjustment of the motor controller.
Disclosure of Invention
The multi-objective optimization method for the motor controller parameters is designed, the ITAE criterion is adopted, the phase shift amount under the tracking of a sine signal and the overshoot amount under a step signal are taken as optimization targets, a multi-objective optimization algorithm is adopted to optimize the control parameters, the Pareto front edge is solved, finally, the final controller parameters are selected by combining the controller parameters and the correlation coefficient matrix of the optimization targets, and the efficiency and the applicability of the motor controller parameter adjustment are improved.
A multi-objective optimization method for motor controller parameters is characterized by comprising the following steps:
step 1, establishing a motor control system according to actual requirements, and determining input quantity and output quantity of a controller;
step 2, setting the parameters of the controller as optimized variables, and setting the value range of the optimized variables;
step 3, setting an ITAE criterion, phase shift amount under the tracking of a sine signal, overshoot under a step signal as an optimization target, and setting a controller constraint condition;
step 4, initializing parameters of the multi-objective optimization algorithm and initializing optimization variables;
step 5, calculating an optimized target value and constraint conditions;
step 7, if the termination condition is met, outputting a Pareto solution set to obtain a Pareto front solution;
step 8, calculating a correlation coefficient matrix of the controller parameters and the optimization target;
and 9, selecting a final controller parameter by combining the Pareto front solution, the controller parameter and the correlation coefficient matrix of the optimization target.
The ITAE criterion in the step 3 is phase shift amount under tracking of sine signalSThe calculation methods of the overshoot σ under the step signal are respectively shown by the following formulas:
in the formula:trepresents time;e(t) represents an error of the displacement;
X=fft(x n,N) （2）
Y=fft(y n,N) （3）
in the formula:x(t) represents a control input signal;y(t) represents a control output signal;f inrepresenting the frequency of the control input signal;f samindicating the period of the control system, and after sampling according to the period,x(t) signal becomesx n，y(t) signal becomesy n，NWhich is indicative of the time of the sampling,N comsubscripts for the frequency components of the input signal in the FFT sequence;
in the formula:x dwhich is indicative of the displacement of the target,x 1indicating the desired displacement.
In the step 3, when the controller constraint condition is that a step signal is input, the overshoot is not more than 1% of the target displacement; wherein, in step 3, the controller restricts that when the sine signal is input, the phase shift amount of the system is not more than 2% of the target phase.
The source of the calculated data in the step 5 is simulation or experiment.
The calculation method of the correlation coefficient matrix of the control parameters and the control system performance in the step 8 is a pearson correlation coefficient analysis method, and a specific formula is as follows:
in the formula:
is a sampleXThe average value of (a) of (b),
is a sampleXThe standard deviation of (a) is determined,
is a sampleYThe average value of (a) of (b),
is a sampleYStandard deviation of (2).
The specific selection method in the step 9 is as follows: and calculating the average value of the correlation degree amplitude of each optimization target and all controller parameters through the correlation coefficient matrix of the control parameters and the optimization targets, and selecting a group of solutions with the maximum average value in Pareto leading edge solutions when the optimization target is optimal as the final controller parameters.
The multi-objective optimization method for the motor controller parameters is designed, the ITAE criterion is adopted, the phase shift amount under the tracking of a sine signal and the overshoot amount under a step signal are taken as optimization targets, a multi-objective optimization algorithm is adopted to optimize the control parameters, the Pareto front edge is solved, finally, the final controller parameters are selected by combining the controller parameters and the correlation coefficient matrix of the optimization targets, and the efficiency and the applicability of the motor controller parameter adjustment are improved.
The invention relates to a multi-objective optimization method of motor controller parameters, which is characterized in that the average value of the correlation degree amplitude of each optimization target and all controller parameters is calculated through a correlation coefficient matrix of the control parameters and the optimization targets, a group of solutions corresponding to the optimal optimization target values in Pareto front-edge solutions is selected as final controller parameters according to the optimization target with the largest average value in the average values of the correlation degree amplitudes of the optimization targets and all controller parameters, the influence of the control parameters on the performance is considered, and the applicability of motor controller parameter adjustment is improved.
The multi-objective optimization method for the motor controller parameters improves the efficiency and the applicability of parameter adjustment of the motor controller, and brings huge economic benefits after being put into industrial application.
Drawings
FIG. 1 is a flow chart of a method for multi-objective optimization of motor controller parameters in accordance with the present invention;
FIG. 2 is a diagram of a multi-objective convergence process of a linear motor displacement controller (using a multi-objective particle swarm optimization) based on a multi-objective optimization method for motor controller parameters;
FIG. 3 is a matrix of correlation coefficients of a linear motor displacement controller parameter and an optimization objective based on a multi-objective optimization method of motor controller parameters;
fig. 4 is a comparison of results under ramp input signals before and after optimization of a certain linear motor displacement controller parameter based on a multi-objective optimization method of motor controller parameters.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a method for multi-objective optimization of motor controller parameters is characterized by comprising the following steps:
step 1, establishing a motor control system according to actual requirements, and determining input quantity and output quantity of a controller;
step 2, setting the parameters of the controller as optimized variables, and setting the value range of the optimized variables;
step 3, setting an ITAE criterion, phase shift amount under the tracking of a sine signal, overshoot under a step signal as an optimization target, and setting a controller constraint condition;
step 4, initializing parameters of the multi-objective optimization algorithm and initializing optimization variables;
step 5, calculating an optimized target value and constraint conditions;
step 7, if the termination condition is met, outputting a Pareto solution set to obtain a Pareto front solution;
step 8, calculating a correlation coefficient matrix of the controller parameters and the optimization target;
and 9, selecting a final controller parameter by combining the Pareto front solution, the controller parameter and the correlation coefficient matrix of the optimization target.
ITAE criterion in step 3, phase shift amount under tracking of sinusoidal signalSThe calculation methods of the overshoot σ under the step signal are respectively shown by the following formulas:
in the formula:trepresents time;e(t) represents an error of the displacement;
X=fft(x n,N) （2）
Y=fft(y n,N) （3）
in the formula:x(t) represents a control input signal;y(t) represents a control output signal;f inrepresenting the frequency of the control input signal;f samindicating the period of the control system, and after sampling according to the period,x(t) signal becomesx n，y(t) signal becomesy n，NWhich is indicative of the time of the sampling,N comsubscripts for the frequency components of the input signal in the FFT sequence;
in the formula:x dwhich is indicative of the displacement of the target,x 1indicating the desired displacement.
Step 3, when the controller constraint condition is step signal input, the overshoot is not more than 1% of the target displacement; wherein, in step 3, the controller restricts that when the sine signal is input, the phase shift amount of the system is not more than 2% of the target phase.
The source of the calculated data in step 5 is simulation or experiment.
The calculation method of the correlation coefficient matrix of the control parameters and the control system performance in the step 8 is a pearson correlation coefficient analysis method, and the specific formula is as follows:
in the formula:
is a sampleXThe average value of (a) of (b),
is a sampleXThe standard deviation of (a) is determined,
is a sampleYThe average value of (a) of (b),
is a sampleYStandard deviation of (2).
The specific selection method in the step 9 is as follows: and calculating the average value of the correlation degree amplitude of each optimization target and all controller parameters through the correlation coefficient matrix of the control parameters and the optimization targets, and selecting a group of solutions with the maximum average value in Pareto leading edge solutions when the optimization target is optimal as the final controller parameters.
An embodiment of the present invention is explained with reference to fig. 2, 3 and 4.
The control object is a voice coil motor of a certain model, and the main parameters of the motor are as follows: moving mass 0.162kg, coil resistance 1.25 omega, coil inductance 0.00132H, damping coefficient 3.11.
The designed controller is a robust controller aiming at position servo, and the control parameters, namely the optimization variables of the controller are as follows: position loop constant gaink 1In a linear feedback termk s1In the robust feedback termk s2(ii) a Current loop constant gaink 2In a linear feedback termk s3In the robust feedback termk s4。
The adopted optimization algorithm is a multi-target particle swarm algorithm, and the main parameters of the algorithm are set as follows: population size 22, iteration number 33, inertia factor
The value is linearly decreased from 1 to 0.4, and the learning factor is increasedc 1,c 2Are all 1.72.
FIG. 2 is a diagram of a multi-objective convergence process of a linear motor displacement controller based on a multi-objective optimization method of motor controller parameters; FIG. 3 is a correlation coefficient matrix of a linear motor displacement controller parameter and an optimization objective for a multi-objective optimization method based on motor controller parameters.
Calculating a correlation coefficient matrix of the controller parameters of the voice coil motor displacement controller and the optimization target according to the data in the figure 3, and finally selecting the main parameters of the controller as follows:k 1in the range of 786, the content of the active carbon,k 2at the point of the interface being 1430,k s1in the order of 7548210, is,k s2is 1501350.
FIG. 4 is a comparison of results of slope input signals before and after optimization of a controller parameter of a linear motor displacement controller based on a multi-objective optimization method of motor controller parameters, wherein the maximum error of robust control before optimization and the time for synchronizing the target displacement with a voice coil motor are 0.21mm and 20.10ms respectively; the optimized robust control maximum error and the time for entering synchronization are respectively 0.267mm and 35.3 ms; compared with the maximum error before optimization, the maximum error is reduced by 21.3%, the synchronization time is improved by 43.0% compared with the maximum error before optimization, and the result shows that the optimized parameters improve the efficiency and the applicability of parameter adjustment of the motor controller, and the effectiveness and the adaptability of optimization are verified.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (6)
1. A multi-objective optimization method for motor controller parameters is characterized by comprising the following steps:
step 1, establishing a motor control system according to actual requirements, and determining input quantity and output quantity of a controller;
step 2, setting the parameters of the controller as optimized variables, and setting the value range of the optimized variables;
step 3, setting an ITAE criterion, phase shift amount under the tracking of a sine signal, overshoot under a step signal as an optimization target, and setting a controller constraint condition;
step 4, initializing parameters of the multi-objective optimization algorithm and initializing optimization variables;
step 5, calculating an optimized target value and constraint conditions;
step 6, if the termination condition is not met, updating the parameters and the optimization variables of the multi-objective optimization algorithm, and returning to the step 5;
step 7, if the termination condition is met, outputting a Pareto solution set to obtain a Pareto front solution;
step 8, calculating a correlation coefficient matrix of the controller parameters and the optimization target;
and 9, selecting a final controller parameter by combining the Pareto front solution, the controller parameter and the correlation coefficient matrix of the optimization target.
2. The method of claim 1 for multi-objective optimization of machine controller parameters, wherein the ITA in step 3E criterion, amount of phase shift under sinusoidal signal trackingSThe calculation methods of the overshoot σ under the step signal are respectively shown by the following formulas:
in the formula:trepresents time;e(t) represents an error of the displacement;
X=fft(x n,N) （2）
Y=fft(y n,N) （3）
in the formula:x(t) represents a control input signal;y(t) represents a control output signal;f inrepresenting the frequency of the control input signal;f samindicating the period of the control system, and after sampling according to the period,x(t) signal becomesx n，y(t) signal becomesy n，NWhich is indicative of the time of the sampling,N comsubscripts for the frequency components of the input signal in the FFT sequence;
in the formula:x dwhich is indicative of the displacement of the target,x 1indicating the desired displacement.
3. The method of claim 1, wherein in step 3, when the controller constraint condition is step signal input, the overshoot is not greater than 1% of the target displacement; wherein, in step 3, the controller restricts that when the sine signal is input, the phase shift amount of the system is not more than 2% of the target phase.
4. The method of claim 1, wherein the source of the calculated data in step 5 is simulation or experiment.
5. The method of claim 1, wherein the calculation method of the correlation coefficient matrix of the control parameters and the control system performance in step 8 is pearson correlation coefficient analysis, and the specific formula is as follows:
in the formula:
is a sampleXThe average value of (a) of (b),
is a sampleXThe standard deviation of (a) is determined,
is a sampleYThe average value of (a) of (b),
is a sampleYStandard deviation of (2).
6. The method of claim 1 for multi-objective optimization of motor controller parameters, wherein the specific selection method in step 9 is: and calculating the average value of the correlation degree amplitude of each optimization target and all controller parameters through the correlation coefficient matrix of the control parameters and the optimization targets, and selecting a group of solutions with the maximum average value in Pareto leading edge solutions when the optimization target is optimal as the final controller parameters.
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Application publication date: 20201127 |