Active-disturbance-rejection control method of permanent magnet synchronous motor and related device

Abstract

The invention discloses an active disturbance rejection control method of a permanent magnet synchronous motor and a related device, and relates to the technical field of motor control, and the method comprises the steps: carrying out the preprocessing of operation data, and constructing a target motor model based on the preprocessed operation data; acquiring first disturbance data by using a nonlinear disturbance estimator based on the operation data; based on the operation data, using a self-learning strategy to obtain second disturbance data so as to generate target disturbance data with the first disturbance data; performing frequency response analysis on the permanent magnet synchronous motor based on the target motor model; constructing an active-disturbance-rejection controller based on the frequency response data in combination with a nonlinear integral feed-forward compensator; and determining a target control quantity based on the active-disturbance-rejection controller and the target disturbance data, and optimizing the target control quantity to perform active-disturbance-rejection processing of the permanent magnet synchronous motor. According to the invention, high-reliability and high-stability active-disturbance-rejection control of the permanent magnet synchronous motor is realized, and the high control requirement of the permanent magnet synchronous motor is met.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular to an auto-disturbance rejection control method and related devices for a permanent magnet synchronous motor. Background Art

[0002] Permanent magnet synchronous motors (PMSMs) are widely used in industrial automation, electric vehicles, aerospace, and other fields due to their high efficiency, high power density, and ease of maintenance. However, various disturbances and measurement noise present in PMSMs, which affect their control performance. Consequently, active disturbance rejection control (ADRC) for PMSMs has received increasing attention. Disturbance analysis is a crucial step in PMSM ADRC. Currently, disturbance analysis primarily relies on conventional observers for disturbance estimation, but this approach suffers from poor disturbance estimation performance, resulting in insufficient reliability for PMSM ADRC and an inability to meet the high control requirements of PMSMs. Furthermore, current PMSM ADRC typically utilizes a traditional proportional-integral controller (PIC). However, this controller is sensitive to design parameters, exhibits poor robustness, and exhibits weak disturbance rejection capabilities, making it difficult to achieve stable control of the PMSM under disturbance conditions. Consequently, ADRC for PMSMs fails to achieve the desired results. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art. The present invention provides a method and related devices for active disturbance rejection control of a permanent magnet synchronous motor, which realizes active disturbance rejection control of the permanent magnet synchronous motor with high reliability and high stability, and meets the high control requirements of the permanent magnet synchronous motor.

[0004] In order to solve the above technical problems, the present invention provides an active disturbance rejection control method for a permanent magnet synchronous motor, the method comprising: Preprocessing the operating data of the permanent magnet synchronous motor to obtain the preprocessed operating data, and constructing a motor model based on the preprocessed operating data to obtain a target motor model; Performing disturbance analysis on the permanent magnet synchronous motor using a nonlinear disturbance estimator based on the preprocessed operating data to obtain first disturbance data; Performing disturbance analysis on the permanent magnet synchronous motor using a self-learning strategy based on the preprocessed operating data to obtain second disturbance data, and generating target disturbance data based on the first disturbance data and the second disturbance data; Perform frequency response analysis on the permanent magnet synchronous motor based on the target motor model to obtain frequency response data; An active disturbance rejection controller is constructed based on frequency response data and a nonlinear integral feedforward compensator. A target control quantity is determined based on an active disturbance rejection controller and target disturbance data, and the target control quantity is optimized to obtain an optimized target control quantity. Active disturbance rejection processing of the permanent magnet synchronous motor is performed based on the optimized target control quantity.

[0005] Optionally, preprocessing the operating data of the permanent magnet synchronous motor to obtain the preprocessed operating data, and constructing a motor model based on the preprocessed operating data to obtain a target motor model includes: Perform data cleaning on the operating data to obtain the cleaned operating data; Perform data integration processing on the operation data after data cleaning to obtain pre-processed operation data; A motor dynamic equation and a torque equation are constructed based on the preprocessed operating data, and a target motor model is constructed based on the motor dynamic equation and the torque equation.

[0006] Optionally, performing disturbance analysis on the permanent magnet synchronous motor using a nonlinear disturbance estimator based on the preprocessed operating data to obtain first disturbance data includes: The target state equations of the stator direct-axis and stator quadrature-axis currents of the permanent magnet synchronous motor are constructed based on the preprocessed operating data; Construct a linear disturbance estimator based on the target state equation; Constructing a nonlinear disturbance estimator based on nonlinear function and observer gain; A disturbance analysis is performed on the permanent magnet synchronous motor based on a linear disturbance estimator and a nonlinear disturbance estimator to obtain first disturbance data.

[0007] Optionally, performing disturbance analysis on the permanent magnet synchronous motor using a self-learning strategy based on the preprocessed operating data to obtain second disturbance data includes: Generate preliminary disturbance estimation data based on pre-processed operating data and historical control input using a preset motor stator current model; A disturbance estimation model is constructed using a fuzzy neural network based on a preset tracker and a preset observer; Based on the preliminary disturbance estimation data, the model parameters of the disturbance estimation model are updated using a self-learning strategy to obtain an updated disturbance estimation model; A disturbance analysis is performed on the permanent magnet synchronous motor based on the updated disturbance estimation model to obtain second disturbance data.

[0008] Optionally, performing frequency response analysis on the permanent magnet synchronous motor based on the target motor model to obtain frequency response data includes: Determine small perturbation simulation strategies and small signal analysis tools based on the target motor model; Based on the small disturbance simulation strategy and small signal analysis tools, disturbance is applied to the target motor model and response data is collected to obtain target response data; Based on the target response data, a frequency response analysis of the permanent magnet synchronous motor is performed using a target frequency component to obtain frequency response data.

[0009] Optionally, constructing an active disturbance rejection controller based on frequency response data in combination with a nonlinear integral feedforward compensator includes: generating a transfer function based on the frequency response data, and constructing a cost function based on the transfer function; Based on the cost function, variable weights are assigned and iteratively optimized to obtain an optimized solution of the cost function. The control parameters of the permanent magnet synchronous motor are determined based on the optimized solution of the cost function, and a proportional-integral-differential PID controller is determined based on the control parameters. A nonlinear integral feedforward compensator is constructed based on a preset proportional gain and a preset integral gain combined with a nonlinear function; An active disturbance rejection controller is constructed based on the PID controller combined with a nonlinear integral feedforward compensator.

[0010] Optionally, optimizing the target control amount to obtain an optimized target control amount includes: Determine the whale optimization algorithm's prey encirclement strategy, bubble net attack strategy, and random search strategy; The refraction principle is set in the whale optimization algorithm, and the target control amount is optimized based on the refraction principle, prey encirclement strategy, bubble net attack strategy and random search strategy of the whale optimization algorithm to obtain the optimized target control amount.

[0011] In addition, the present invention also provides an active disturbance rejection control device for a permanent magnet synchronous motor, the device comprising: Motor model building module: used to preprocess the operating data of the permanent magnet synchronous motor to obtain the preprocessed operating data, and build the motor model based on the preprocessed operating data to obtain the target motor model; A first disturbance analysis module is configured to perform disturbance analysis on the permanent magnet synchronous motor using a nonlinear disturbance estimator based on the preprocessed operating data to obtain first disturbance data; A second disturbance analysis module is configured to perform disturbance analysis on the permanent magnet synchronous motor using a self-learning strategy based on the preprocessed operating data, obtain second disturbance data, and generate target disturbance data based on the first disturbance data and the second disturbance data; Frequency response analysis module: used to perform frequency response analysis on the permanent magnet synchronous motor based on the target motor model and obtain frequency response data; Controller building module: used to build an active disturbance rejection controller based on frequency response data combined with a nonlinear integral feedforward compensator; Motor active disturbance rejection module: used to determine the target control quantity based on the active disturbance rejection controller and target disturbance data, optimize the target control quantity to obtain the optimized target control quantity, and perform active disturbance rejection processing on the permanent magnet synchronous motor based on the optimized target control quantity.

[0012] In addition, the present invention also provides an electronic device, which includes a processor and a memory, the memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the above-mentioned permanent magnet synchronous motor self-disturbance rejection control method.

[0013] In addition, the present invention also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the above-mentioned self-disturbance rejection control method of the permanent magnet synchronous motor.

[0014] In an embodiment of the present invention, a nonlinear disturbance estimator is used to perform disturbance analysis on a permanent magnet synchronous motor based on preprocessed operating data to obtain first disturbance data. A self-learning strategy is used to perform disturbance analysis on the permanent magnet synchronous motor based on the preprocessed operating data to obtain second disturbance data. Target disturbance data is generated based on the first disturbance data and the second disturbance data, making the obtained target disturbance data more accurate while overcoming the limitations of a single disturbance analysis. An auto-disturbance rejection controller is constructed based on frequency response data in combination with a nonlinear integral feedforward compensator, making the constructed auto-disturbance rejection controller more robust and capable of resisting disturbances. A target control quantity is determined based on the auto-disturbance rejection controller and the target disturbance data, the target control quantity is optimized to obtain an optimized target control quantity, and auto-disturbance rejection processing of the permanent magnet synchronous motor is performed based on the optimized target control quantity, thereby achieving high-reliability and high-stability auto-disturbance rejection control of the permanent magnet synchronous motor, meeting the high control requirements of the permanent magnet synchronous motor, and achieving a more ideal effect of the auto-disturbance rejection control of the permanent magnet synchronous motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 1 is a flow chart of an active disturbance rejection control method for a permanent magnet synchronous motor in an embodiment of the present invention; Figure 2 is a flow chart of an active disturbance rejection control method for a permanent magnet synchronous motor in another embodiment of the present invention; Figure 3Schematic diagram of the structure of the active disturbance rejection control device of the permanent magnet synchronous motor in an embodiment of the present invention; Figure 4 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] Example 1 See also Figure 1 , Figure 1 FIG. 5 is a flow chart of an active disturbance rejection control method for a permanent magnet synchronous motor in an embodiment of the present invention, wherein the method comprises: S11: preprocessing the operating data of the permanent magnet synchronous motor to obtain the preprocessed operating data, and constructing a motor model based on the preprocessed operating data to obtain a target motor model; In the specific implementation process of the present invention, the operating data of the permanent magnet synchronous motor is preprocessed to obtain the preprocessed operating data, and a motor model is constructed based on the preprocessed operating data to obtain a target motor model, including: performing data cleaning processing on the operating data to obtain the operating data after data cleaning processing; performing data integration processing on the operating data after data cleaning processing to obtain the preprocessed operating data; constructing the motor dynamic equation and the torque equation based on the preprocessed operating data, and constructing the target motor model based on the motor dynamic equation and the torque equation.

[0019] Specifically, the operating data of the permanent magnet synchronous motor includes parameters such as inductance, current, voltage, resistance, torque and speed. The operating data is cleaned, that is, the operating data is cleaned of garbled or abnormal characters to avoid affecting subsequent data analysis, and the operating data after data cleaning is obtained. The operating data after data cleaning is integrated, that is, the operating data after data cleaning is formatted and classified, that is, the format of the operating data is converted into a standard format, and the operating data of the same type are classified to improve the efficiency of data analysis and obtain pre-processed operating data. The motor dynamic equation and torque equation are constructed based on the pre-processed operating data. The motor dynamic equation is used to reveal the electrical characteristics of the motor and also connects the relationship between electrical input and mechanical input. The expression of the motor dynamic equation can be: , Among them, V is the voltage of the electromagnetic synchronous motor, R is the resistance, I is the current, and L is the inductance. is the time derivative of the current, E is the potential generated by the rotation of the permanent magnet synchronous motor; The torque equation is constructed using the torque, moment of inertia, angular velocity, and damping coefficient from the preprocessed operating data. The torque equation reflects the behavior of the permanent magnet synchronous motor under mechanical load and is the key to connecting electrical input and mechanical motion. A target motor model is constructed based on the motor dynamic equation and the torque equation. That is, a closed-loop motor model is formed by the motor dynamic equation and the torque equation, which is the target motor model. This target motor model can simulate and predict the response of the permanent magnet synchronous motor under different operating conditions.

[0020] S12: performing disturbance analysis on the permanent magnet synchronous motor using a nonlinear disturbance estimator based on the preprocessed operating data to obtain first disturbance data; In the specific implementation process of the present invention, the nonlinear disturbance estimator is used to perform disturbance analysis on the permanent magnet synchronous motor based on the preprocessed operating data to obtain the first disturbance data, including: constructing the target state equations of the stator direct-axis and stator quadrature-axis currents of the permanent magnet synchronous motor based on the preprocessed operating data; constructing a linear disturbance estimator based on the target state equation; constructing a nonlinear disturbance estimator based on the nonlinear function and the observer gain; and performing disturbance analysis on the permanent magnet synchronous motor based on the linear disturbance estimator and the nonlinear disturbance estimator to obtain the first disturbance data.

[0021] Specifically, the target state equations of the stator direct-axis and stator quadrature-axis currents of the permanent magnet synchronous motor are constructed based on the preprocessed operating data, and the voltage value, inductance, current value, stator resistance value, stator direct-axis and quadrature-axis flux and rotor flux angular velocity of the stator direct-axis and stator quadrature-axis of the permanent magnet synchronous motor are extracted from the preprocessed operating data. The stator voltage equation is constructed according to the voltage value, current value, stator resistance value, stator direct-axis and quadrature-axis flux and rotor flux angular velocity of the stator direct-axis and stator quadrature-axis. The stator flux equation is constructed according to the inductance of the stator direct-axis and stator quadrature-axis and the permanent magnet flux. The stator flux equation is substituted into the stator voltage equation, and the stator current equation is determined in combination with the voltage-current-inductance relationship. The system effect caused by fixing the direct-axis time-varying inductance to a constant inductance and the quadrature-axis coupling term is determined. The first modelable disturbance quantity of the direct axis determines the second modelable disturbance quantity of the quadrature axis of the system caused by fixing the quadrature axis time-varying inductance to a constant inductance and the direct axis coupling term. The quadrature-direct axis coupling term is one of the more complex influencing factors in the control of the permanent magnet synchronous motor. It represents the interaction between the components of the torque command on the quadrature axis and the direct axis. Specifically, when torque is applied to the quadrature axis, a certain reaction torque will also be generated on the direct axis, and vice versa. According to the first disturbance quantity and the second disturbance quantity, the stator current equation is combined to construct a target state equation, and a linear disturbance estimator is constructed based on the target state equation. The linear disturbance term is used as an expanded state variable to expand the target state equation to obtain the expanded target state equation, and a linear disturbance estimator is constructed based on the expanded target state equation. A nonlinear disturbance estimator is constructed based on a nonlinear function and an observer gain. The expression of the nonlinear disturbance estimator can be: , Among them, Z is the intermediate variable of the nonlinear disturbance estimator, l(x) is the observer gain, is a nonlinear function, F is the total disturbance estimate, is the system state gain, U is the voltage, is the first-order derivative of Z. A disturbance analysis is performed on the permanent magnet synchronous motor based on the linear disturbance estimator and the nonlinear disturbance estimator, that is, disturbance estimation is performed on the permanent magnet synchronous motor using the linear disturbance estimator and the nonlinear disturbance estimator to obtain a disturbance estimation value, that is, obtain first disturbance data, which may include the linear disturbance estimation value and the nonlinear disturbance estimation value.

[0022] S13: performing disturbance analysis on the permanent magnet synchronous motor using a self-learning strategy based on the preprocessed operating data to obtain second disturbance data, and generating target disturbance data based on the first disturbance data and the second disturbance data; In the specific implementation process of the present invention, the disturbance analysis of the permanent magnet synchronous motor is performed based on the preprocessed operating data using a self-learning strategy to obtain second disturbance data, including: generating preliminary disturbance estimation data based on the preprocessed operating data in combination with historical control input using a preset motor stator current model; constructing a disturbance estimation model based on a preset tracker and a preset observer using a fuzzy neural network; updating the model parameters of the disturbance estimation model based on the preliminary disturbance estimation data using a self-learning strategy to obtain an updated disturbance estimation model; and performing disturbance analysis on the permanent magnet synchronous motor based on the updated disturbance estimation model to obtain second disturbance data.

[0023] Specifically, preliminary disturbance estimation data is generated based on preprocessed operating data combined with historical control inputs using a preset motor stator current model. The historical control inputs are control inputs at target control time points in past control cycles. The control inputs are determined by combining the reference inputs of a basic controller with the current at the target control time point of the permanent magnet synchronous motor. The basic controller is a proportional-integral controller. The first-order derivative of the permanent magnet synchronous motor's current with respect to time is determined based on the preprocessed operating data. Based on the first-order derivative, the motor current, and the historical control inputs, a disturbance is calculated using a preset motor stator current mathematical model. The disturbance calculation result is used as a preliminary disturbance estimation value, i.e., preliminary disturbance estimation data. A disturbance estimation model is constructed using a fuzzy neural network based on a preset tracker and a preset observer. Operation of the permanent magnet synchronous motor is simulated using the preset tracker and the preset observer. The output signals of the preset tracker and the preset observer are recorded. The output signals of the preset tracker and the preset observer are used as sample data sets. The fuzzy neural network is trained using the sample data sets to obtain a trained fuzzy neural network, which is used as the disturbance estimation model. Based on preliminary disturbance estimation data, a self-learning strategy is used to update the model parameters of a disturbance estimation model. The current at a target control time point of the permanent magnet synchronous motor is input into the disturbance estimation model to obtain output disturbance estimation data. The difference between the output disturbance estimation data and the preliminary disturbance estimation data is calculated, and it is determined whether the difference is greater than or equal to a preset error threshold. If the difference is less than the preset error threshold, the disturbance estimation model does not need to update the model parameters. If the difference is greater than or equal to the preset error threshold, the disturbance estimation model parameters are updated using a self-learning strategy. Relevant knowledge is classified and summarized in a knowledge database, and a parameter self-learning controller is set. The parameter self-learning controller uses the relevant knowledge to perform online self-learning on the disturbance estimation model to update the model parameters, thereby obtaining an updated disturbance estimation model, thereby improving the performance and accuracy of the disturbance estimation model. Based on the updated disturbance estimation model, a disturbance analysis is performed on the permanent magnet synchronous motor. The updated disturbance estimation model is combined with input parameters of the permanent magnet synchronous motor, which may include the current of the permanent magnet synchronous motor, to obtain corresponding disturbance estimation values, which may include nonlinear disturbance estimation values and linear disturbance estimation values, i.e., second disturbance data. The target disturbance data is generated based on the first disturbance data and the second disturbance data. The target disturbance data can be obtained by performing a mean operation on the first disturbance data and the second disturbance data, thereby avoiding the limitations and one-sidedness brought about by a single disturbance analysis and making the obtained target disturbance data more in line with the actual situation.

[0024] S14: Perform frequency response analysis on the permanent magnet synchronous motor based on the target motor model to obtain frequency response data; In the specific implementation process of the present invention, the frequency response analysis of the permanent magnet synchronous motor is performed based on the target motor model to obtain frequency response data, including: determining a small disturbance simulation strategy and a small signal analysis tool based on the target motor model; applying disturbance and collecting response data to the target motor model based on the small disturbance simulation strategy and the small signal analysis tool to obtain target response data; and performing frequency response analysis on the permanent magnet synchronous motor using a target frequency component based on the target response data to obtain frequency response data.

[0025] Specifically, a small perturbation simulation strategy and a small signal analysis tool are determined based on the target motor model. An appropriate small signal analysis tool is selected based on the target motor model. Small signal analysis tools, such as the Simulink visual simulation tool, can simulate the response of a permanent magnet synchronous motor to electrical or mechanical disturbances. The small perturbation simulation strategy is to set a simulation strategy to impose small electrical or mechanical disturbances on the permanent magnet synchronous motor, such as small voltage changes or load disturbances. Based on the small perturbation simulation strategy and the small signal analysis tool, disturbances are applied to the target motor model and response data is collected. The small perturbation simulation strategy and the target motor model are deployed in the small signal analysis tool. The disturbance application is simulated on the permanent magnet synchronous motor in the small signal analysis tool. After the simulation is applied, the response data of the motor is collected to obtain the target response data. The response data may include changes in voltage, current, speed, and torque. Based on the target response data, the frequency response analysis of the permanent magnet synchronous motor is performed using the target frequency component. The target frequency component is an analysis component of Fourier transform and Laplace transform. That is, the Fourier transform analysis component deployed by the target frequency component converts the target response data from the time domain to the frequency domain representation, that is, the frequency response data is obtained. By analyzing the response at different frequencies, the behavior of the permanent magnet synchronous motor at different frequencies, such as its damping characteristics, can be known. The frequency response data is related to the stability of the motor.

[0026] S15: Build an active disturbance rejection controller based on frequency response data combined with a nonlinear integral feedforward compensator; In the specific implementation process of the present invention, the method of constructing an auto-disturbance rejection controller based on frequency response data in combination with a nonlinear integral feedforward compensator includes: generating a transfer function based on the frequency response data, and constructing a cost function based on the transfer function; performing variable weight allocation and iterative optimization based on the cost function to obtain an optimized solution of the cost function, and determining the control parameters of the permanent magnet synchronous motor based on the optimized solution of the cost function, and determining a proportional-integral-differential PID controller based on the control parameters; constructing a nonlinear integral feedforward compensator based on a preset proportional gain and a preset integral gain in combination with a nonlinear function; and constructing an auto-disturbance rejection controller based on a PID controller combined with a nonlinear integral feedforward compensator.

[0027] Specifically, a transfer function is generated based on the frequency response data, and the transfer function is generated through the conversion relationship between the frequency response data and the transfer function. The transfer function is the ratio of the Laplace transform of the system output to the Laplace transform of the input quantity, and a cost function is constructed based on the transfer function. The transfer function is obtained by analyzing the response of the permanent magnet synchronous motor under small signal disturbances, and can characterize the stability and response speed of the permanent magnet synchronous motor at different frequencies. For example, the amplitude and phase of the transfer function can determine the resonant frequency and damping ratio, which are key parameters for evaluating the stability of the permanent magnet synchronous motor. The performance parameters of the permanent magnet synchronous motor under disturbance are determined according to the transfer function, such as response time, stability, etc., and a cost function is constructed based on the performance parameters. Variable weight assignment and iterative optimization are performed based on the cost function. Variable weight assignment is performed on the cost function, that is, weight assignment is performed on the variables in the cost function, such as the damping ratio in the cost function is assigned the first weight, the response time in the cost function is assigned the second weight, etc. Different variables are assigned different weights, and the weight assignment reflects the importance of performance parameters. After the cost function is assigned variable weights, the cost function after variable weight assignment is iteratively optimized. The iterative optimization can use a genetic algorithm, and the motor control parameters are used as population individuals. The fitness function is set through the cost function, and iterative optimization is performed according to the fitness function and the population individuals until the preset number of iterations is reached, that is, the control parameters are continuously adjusted according to the fitness function until the parameter combination that minimizes the cost function is searched, so as to realize the search for the global optimal solution of the cost function, and the optimal solution of the cost function is used as the cost function optimization solution, and the permanent magnet synchronous motor is determined based on the cost function optimization solution. The control parameters of the motor, that is, the optimal solution of the cost function is decoded to obtain the control parameters of the permanent magnet synchronous motor, such as the speed control coefficient and the torque control coefficient. Based on the control parameters, a proportional-integral-differential (PID) controller is determined. The proportional coefficient, integral coefficient, and differential coefficient of the PID controller are determined by the control parameters. The controller coefficients can be determined by the control parameters using the Ziegler-Nichols method. The Ziegler-Nichols method is a method for tuning the PID controller and exploring the control coefficients of the PID controller. Based on the dynamic response characteristics of the system, the control parameters are deployed in the simulation software. The proportional coefficient, integral coefficient, and differential coefficient of the PID controller are determined through simulation experiments in the simulation software. After determining the proportional coefficient, integral coefficient, and differential coefficient, the final PID controller can be constructed. A nonlinear integral feedforward compensator is constructed based on a preset proportional gain and a preset integral gain combined with a nonlinear function. The expression of the nonlinear integral feedforward compensator is: , Where u is a nonlinear integral feedforward compensator, is the preset proportional gain, is the preset integral gain, and g(e) is a nonlinear function. An ADRC is constructed based on a PID controller combined with a nonlinear integral feedforward compensator. A feedforward compensation channel is introduced into the PID controller, and a nonlinear integral feedforward compensator is deployed within the feedforward compensation channel to form an ADRC. The nonlinear integral feedforward compensator can avoid overshoot problems caused by excessive errors in the integral. Furthermore, the introduction of the nonlinear integral feedforward compensator can enhance the response speed and interference rejection capability of the ADRC. The combination of the PID controller and the nonlinear integral feedforward compensator can achieve higher accuracy in the constructed ADRC.

[0028] S16: Determine a target control quantity based on the active disturbance rejection controller and the target disturbance data, optimize the target control quantity to obtain an optimized target control quantity, and perform active disturbance rejection processing on the permanent magnet synchronous motor based on the optimized target control quantity.

[0029] In the specific implementation process of the present invention, the target control quantity is optimized to obtain the optimized target control quantity, including: determining the prey encirclement strategy, bubble net attack strategy and random search strategy of the whale optimization algorithm; setting the refraction principle in the whale optimization algorithm, and optimizing the target control quantity based on the refraction principle, prey encirclement strategy, bubble net attack strategy and random search strategy of the whale optimization algorithm to obtain the optimized target control quantity.

[0030] Specifically, the target control variable is determined based on the active disturbance rejection controller and target disturbance data. The target disturbance data is input into the active disturbance rejection controller to obtain the target control variable, such as the voltage of the stator direct axis and the stator quadrature axis. The whale optimization algorithm's encirclement strategy, bubble net attack strategy, and random search strategy are determined. The whale optimization algorithm is a metaheuristic algorithm that simulates the hunting behavior of whale groups. Its core concept is to achieve global optimization through three strategies: encirclement, bubble net attack, and random search. The encirclement strategy updates the individual position based on the current optimal solution. The bubble net attack strategy simulates a local fine search. The random search strategy randomly selects an individual for global exploration. The refraction principle is set in the whale optimization algorithm. The refraction principle means that when light is emitted from one medium to another, the propagation direction will change. The refraction principle is set in the whale optimization algorithm to simulate the whale searching in different search space media. The search direction changes like the refraction of light, so that the search space can be effectively explored to avoid falling into the local optimum. The target control amount is optimized based on the refraction principle, the prey encirclement strategy, the bubble net attack strategy and the random search strategy of the whale optimization algorithm. After the refraction principle is introduced, the refraction direction obtained by the refraction principle is consistent with the refraction direction obtained by the prey encirclement strategy, the bubble net attack strategy and the random search strategy. The position update methods obtained by the attack strategy and the random search strategy are combined to update the individual position, so that the current solution jumps out of the local optimum and obtains a new current optimal solution. The target control quantity is iteratively optimized to the preset number of iterations through the whale optimization algorithm to obtain the optimized target control quantity. Based on the optimized target control quantity, the permanent magnet synchronous motor is subjected to self-anti-disturbance processing. By optimizing the target control quantity, the permanent magnet synchronous motor is suppressed and eliminated for disturbances, which can maximize the elimination of the influence of disturbances on the permanent magnet synchronous motor, realize stable control of the permanent magnet synchronous motor under disturbance state, and improve the performance level of the permanent magnet synchronous motor.

[0031] In an embodiment of the present invention, a nonlinear disturbance estimator is used to perform disturbance analysis on a permanent magnet synchronous motor based on preprocessed operating data to obtain first disturbance data. A self-learning strategy is used to perform disturbance analysis on the permanent magnet synchronous motor based on the preprocessed operating data to obtain second disturbance data. Target disturbance data is generated based on the first disturbance data and the second disturbance data, making the obtained target disturbance data more accurate while overcoming the limitations of a single disturbance analysis. An auto-disturbance rejection controller is constructed based on frequency response data in combination with a nonlinear integral feedforward compensator, making the constructed auto-disturbance rejection controller more robust and capable of resisting disturbances. A target control quantity is determined based on the auto-disturbance rejection controller and the target disturbance data, the target control quantity is optimized to obtain an optimized target control quantity, and auto-disturbance rejection processing of the permanent magnet synchronous motor is performed based on the optimized target control quantity, thereby achieving high-reliability and high-stability auto-disturbance rejection control of the permanent magnet synchronous motor, meeting the high control requirements of the permanent magnet synchronous motor, and achieving a more ideal effect of the auto-disturbance rejection control of the permanent magnet synchronous motor.

[0032] Example 2 See also Figure 2 , Figure 2 FIG. 5 is a flow chart of an active disturbance rejection control method for a permanent magnet synchronous motor in another embodiment of the present invention, the method comprising: S201: Preprocessing the operating data of the permanent magnet synchronous motor to obtain the preprocessed operating data, and constructing a motor model based on the preprocessed operating data to obtain a target motor model; S202: performing disturbance analysis on the permanent magnet synchronous motor using a nonlinear disturbance estimator based on the preprocessed operating data to obtain first disturbance data; S203: Generate preliminary disturbance estimation data based on the preprocessed operating data and historical control input using a preset motor stator current model; S204: constructing a disturbance estimation model using a fuzzy neural network based on a preset tracker and a preset observer; S205: Inputting the current of the permanent magnet synchronous motor at the target control time point into the disturbance estimation model to obtain output disturbance estimation data, and calculating the difference between the output disturbance estimation data and the preliminary disturbance estimation data; S206: Determine whether the difference is greater than or equal to a preset error threshold; S207: If the difference is less than the preset error threshold, the disturbance estimation model does not need to update the model parameters, and the disturbance estimation model can be equivalent to the updated disturbance estimation model; S208: If the difference is greater than or equal to a preset error threshold, updating the model parameters of the disturbance estimation model through a self-learning strategy to obtain an updated disturbance estimation model; S209: performing disturbance analysis on the permanent magnet synchronous motor based on the updated disturbance estimation model to obtain second disturbance data, and generating target disturbance data based on the first disturbance data and the second disturbance data; S210: Perform frequency response analysis on the permanent magnet synchronous motor based on the target motor model to obtain frequency response data; S211: Build an active disturbance rejection controller based on frequency response data combined with a nonlinear integral feedforward compensator; S212: Determine a target control quantity based on the active disturbance rejection controller and the target disturbance data, optimize the target control quantity to obtain an optimized target control quantity, and perform active disturbance rejection processing on the permanent magnet synchronous motor based on the optimized target control quantity.

[0033] In an embodiment of the present invention, a nonlinear disturbance estimator is used to perform disturbance analysis on a permanent magnet synchronous motor based on preprocessed operating data to obtain first disturbance data. A self-learning strategy is used to perform disturbance analysis on the permanent magnet synchronous motor based on the preprocessed operating data to obtain second disturbance data. Target disturbance data is generated based on the first disturbance data and the second disturbance data, making the obtained target disturbance data more accurate while overcoming the limitations of a single disturbance analysis. An auto-disturbance rejection controller is constructed based on frequency response data in combination with a nonlinear integral feedforward compensator, making the constructed auto-disturbance rejection controller more robust and capable of resisting disturbances. A target control quantity is determined based on the auto-disturbance rejection controller and the target disturbance data, the target control quantity is optimized to obtain an optimized target control quantity, and auto-disturbance rejection processing of the permanent magnet synchronous motor is performed based on the optimized target control quantity, thereby achieving high-reliability and high-stability auto-disturbance rejection control of the permanent magnet synchronous motor, meeting the high control requirements of the permanent magnet synchronous motor, and achieving a more ideal effect of the auto-disturbance rejection control of the permanent magnet synchronous motor.

[0034] Example 3 See also Figure 3 , Figure 3 : is a schematic diagram of the structure of an active disturbance rejection control device for a permanent magnet synchronous motor in an embodiment of the present invention, the device comprising: The motor model building module 31 is used to pre-process the operating data of the permanent magnet synchronous motor to obtain the pre-processed operating data, and to build a motor model based on the pre-processed operating data to obtain a target motor model; A first disturbance analysis module 32 is configured to perform disturbance analysis on the permanent magnet synchronous motor using a nonlinear disturbance estimator based on the preprocessed operating data to obtain first disturbance data; The second disturbance analysis module 33 is configured to perform disturbance analysis on the permanent magnet synchronous motor using a self-learning strategy based on the pre-processed operating data, obtain second disturbance data, and generate target disturbance data based on the first disturbance data and the second disturbance data; Frequency response analysis module 34: used to perform frequency response analysis on the permanent magnet synchronous motor based on the target motor model to obtain frequency response data; Controller construction module 35: used for constructing an active disturbance rejection controller based on frequency response data and a nonlinear integral feedforward compensator; The motor auto-disturbance rejection module 36 is used to determine the target control amount based on the auto-disturbance rejection controller and the target disturbance data, optimize the target control amount to obtain the optimized target control amount, and perform auto-disturbance rejection processing on the permanent magnet synchronous motor based on the optimized target control amount.

[0035] In the specific implementation process of the present invention, the specific implementation method of the device item can refer to the implementation method of the above-mentioned method item, which will not be repeated here.

[0036] In an embodiment of the present invention, a nonlinear disturbance estimator is used to perform disturbance analysis on a permanent magnet synchronous motor based on preprocessed operating data to obtain first disturbance data. A self-learning strategy is used to perform disturbance analysis on the permanent magnet synchronous motor based on the preprocessed operating data to obtain second disturbance data. Target disturbance data is generated based on the first disturbance data and the second disturbance data, making the obtained target disturbance data more accurate while overcoming the limitations of a single disturbance analysis. An auto-disturbance rejection controller is constructed based on frequency response data in combination with a nonlinear integral feedforward compensator, making the constructed auto-disturbance rejection controller more robust and capable of resisting disturbances. A target control quantity is determined based on the auto-disturbance rejection controller and the target disturbance data, the target control quantity is optimized to obtain an optimized target control quantity, and auto-disturbance rejection processing of the permanent magnet synchronous motor is performed based on the optimized target control quantity, thereby achieving high-reliability and high-stability auto-disturbance rejection control of the permanent magnet synchronous motor, meeting the high control requirements of the permanent magnet synchronous motor, and achieving a more ideal effect of the auto-disturbance rejection control of the permanent magnet synchronous motor.

[0037] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the program implements the active disturbance rejection control method for a permanent magnet synchronous motor according to any of the above-described embodiments. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic cards, or optical cards. In other words, a storage device includes any medium that can store or transmit information in a readable form by a device (e.g., a computer or a mobile phone), and can be a read-only memory, a disk, or an optical disk.

[0038] Example 4 See also Figure 4 , Figure 4 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention.

[0039] The embodiment of the present invention further provides an electronic device, such as Figure 4 As shown, the electronic device includes a memory 41, a processor 43, and a computer program 42 stored in the memory 41 and executable on the processor 43. It will be understood by those skilled in the art that Figure 3The electronic devices shown do not constitute a limitation on all devices and may include more or fewer components than shown, or combinations of certain components. The memory 41 can be used to store the computer program 42 and various functional modules, and the processor 43 runs the computer program 42 stored in the memory 41, thereby executing various functional applications and data processing of the device. The memory can be internal memory or external memory, or include both internal memory and external memory. The internal memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or random access memory. The external memory can include a hard disk, floppy disk, ZIP disk, USB flash drive, magnetic tape, etc. The processor 43 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, a single-chip microcomputer, or processor 43, or any conventional processor. The processor and memory disclosed in the present invention include but are not limited to these types of processors and memories. The processor and memory disclosed in the present invention are only examples and not limitations.

[0040] As an embodiment, the electronic device includes: one or more processors 43, a memory 41, and one or more computer programs 42, wherein the one or more computer programs 42 are stored in the memory 41 and are configured to be executed by the one or more processors 43, and the one or more computer programs 42 are configured to execute the self-disturbance rejection control method of the permanent magnet synchronous motor in any of the above embodiments. For the specific implementation process, please refer to the above embodiments and will not be repeated here.

[0041] In an embodiment of the present invention, a nonlinear disturbance estimator is used to perform disturbance analysis on a permanent magnet synchronous motor based on preprocessed operating data to obtain first disturbance data. A self-learning strategy is used to perform disturbance analysis on the permanent magnet synchronous motor based on the preprocessed operating data to obtain second disturbance data. Target disturbance data is generated based on the first disturbance data and the second disturbance data, making the obtained target disturbance data more accurate while overcoming the limitations of a single disturbance analysis. An auto-disturbance rejection controller is constructed based on frequency response data in combination with a nonlinear integral feedforward compensator, making the constructed auto-disturbance rejection controller more robust and capable of resisting disturbances. A target control quantity is determined based on the auto-disturbance rejection controller and the target disturbance data, the target control quantity is optimized to obtain an optimized target control quantity, and auto-disturbance rejection processing of the permanent magnet synchronous motor is performed based on the optimized target control quantity, thereby achieving high-reliability and high-stability auto-disturbance rejection control of the permanent magnet synchronous motor, meeting the high control requirements of the permanent magnet synchronous motor, and achieving a more ideal effect of the auto-disturbance rejection control of the permanent magnet synchronous motor.

[0042] In addition, the above is a detailed introduction to the self-disturbance rejection control method and related devices of a permanent magnet synchronous motor provided by an embodiment of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for controlling an active disturbance rejection of a permanent magnet synchronous motor, characterized in that: The method comprises: Preprocessing the operating data of the permanent magnet synchronous motor to obtain the preprocessed operating data, and constructing a motor model based on the preprocessed operating data to obtain a target motor model; Performing disturbance analysis on the permanent magnet synchronous motor using a nonlinear disturbance estimator based on the preprocessed operating data to obtain first disturbance data; Performing disturbance analysis on the permanent magnet synchronous motor using a self-learning strategy based on the preprocessed operating data to obtain second disturbance data, and generating target disturbance data based on the first disturbance data and the second disturbance data; Perform frequency response analysis on the permanent magnet synchronous motor based on the target motor model to obtain frequency response data; An active disturbance rejection controller is constructed based on frequency response data and a nonlinear integral feedforward compensator. A target control quantity is determined based on an active disturbance rejection controller and target disturbance data, and the target control quantity is optimized to obtain an optimized target control quantity. Active disturbance rejection processing of the permanent magnet synchronous motor is performed based on the optimized target control quantity.

2. The active disturbance rejection control method of a permanent magnet synchronous motor according to claim 1, characterized in that: The preprocessing of the operating data of the permanent magnet synchronous motor to obtain the preprocessed operating data, and constructing a motor model based on the preprocessed operating data to obtain a target motor model includes: Perform data cleaning on the operating data to obtain the cleaned operating data; Perform data integration processing on the operation data after data cleaning to obtain pre-processed operation data; A motor dynamic equation and a torque equation are constructed based on the preprocessed operating data, and a target motor model is constructed based on the motor dynamic equation and the torque equation.

3. The active disturbance rejection control method of a permanent magnet synchronous motor according to claim 1, characterized in that: The method of performing disturbance analysis on the permanent magnet synchronous motor using a nonlinear disturbance estimator based on the preprocessed operating data to obtain first disturbance data includes: The target state equations of the stator direct-axis and stator quadrature-axis currents of the permanent magnet synchronous motor are constructed based on the preprocessed operating data; Construct a linear disturbance estimator based on the target state equation; Constructing a nonlinear disturbance estimator based on nonlinear function and observer gain; A disturbance analysis is performed on the permanent magnet synchronous motor based on a linear disturbance estimator and a nonlinear disturbance estimator to obtain first disturbance data.

4. The active disturbance rejection control method of a permanent magnet synchronous motor according to claim 1, characterized in that: The method of performing disturbance analysis on the permanent magnet synchronous motor using a self-learning strategy based on the pre-processed operating data to obtain second disturbance data includes: Generate preliminary disturbance estimation data based on pre-processed operating data and historical control input using a preset motor stator current model; A disturbance estimation model is constructed using a fuzzy neural network based on a preset tracker and a preset observer; Based on the preliminary disturbance estimation data, the model parameters of the disturbance estimation model are updated using a self-learning strategy to obtain an updated disturbance estimation model; A disturbance analysis is performed on the permanent magnet synchronous motor based on the updated disturbance estimation model to obtain second disturbance data.

5. The active disturbance rejection control method of a permanent magnet synchronous motor according to claim 1, characterized in that: The performing frequency response analysis on the permanent magnet synchronous motor based on the target motor model to obtain frequency response data includes: Determine small perturbation simulation strategies and small signal analysis tools based on the target motor model; Based on the small disturbance simulation strategy and small signal analysis tools, disturbance is applied to the target motor model and response data is collected to obtain target response data; Based on the target response data, a frequency response analysis of the permanent magnet synchronous motor is performed using a target frequency component to obtain frequency response data.

6. The active disturbance rejection control method of a permanent magnet synchronous motor according to claim 1, characterized in that: The active disturbance rejection controller is constructed based on the frequency response data and the nonlinear integral feedforward compensator, including: generating a transfer function based on the frequency response data, and constructing a cost function based on the transfer function; Based on the cost function, variable weights are assigned and iteratively optimized to obtain an optimized solution of the cost function. The control parameters of the permanent magnet synchronous motor are determined based on the optimized solution of the cost function, and a proportional-integral-differential PID controller is determined based on the control parameters. A nonlinear integral feedforward compensator is constructed based on a preset proportional gain and a preset integral gain combined with a nonlinear function; An active disturbance rejection controller is constructed based on the PID controller combined with a nonlinear integral feedforward compensator.

7. The active disturbance rejection control method of a permanent magnet synchronous motor according to claim 1, characterized in that: Optimizing the target control amount to obtain the optimized target control amount includes: Determine the whale optimization algorithm's prey encirclement strategy, bubble net attack strategy, and random search strategy; The refraction principle is set in the whale optimization algorithm, and the target control amount is optimized based on the refraction principle, prey encirclement strategy, bubble net attack strategy and random search strategy of the whale optimization algorithm to obtain the optimized target control amount.

8. An active disturbance rejection control device for a permanent magnet synchronous motor, characterized in that: The device comprises: Motor model building module: used to preprocess the operating data of the permanent magnet synchronous motor to obtain the preprocessed operating data, and build the motor model based on the preprocessed operating data to obtain the target motor model; A first disturbance analysis module is configured to perform disturbance analysis on the permanent magnet synchronous motor using a nonlinear disturbance estimator based on the preprocessed operating data to obtain first disturbance data; A second disturbance analysis module is configured to perform disturbance analysis on the permanent magnet synchronous motor using a self-learning strategy based on the preprocessed operating data, obtain second disturbance data, and generate target disturbance data based on the first disturbance data and the second disturbance data; Frequency response analysis module: used to perform frequency response analysis on the permanent magnet synchronous motor based on the target motor model and obtain frequency response data; Controller building module: used to build an active disturbance rejection controller based on frequency response data combined with a nonlinear integral feedforward compensator; Motor active disturbance rejection module: used to determine the target control quantity based on the active disturbance rejection controller and target disturbance data, optimize the target control quantity to obtain the optimized target control quantity, and perform active disturbance rejection processing on the permanent magnet synchronous motor based on the optimized target control quantity.

9. An electronic device comprising a processor and a memory, characterized in that: The memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the active disturbance rejection control method of the permanent magnet synchronous motor according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the active disturbance rejection control method for a permanent magnet synchronous motor according to any one of claims 1 to 7.

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