Actuator control parameter self-tuning method, device, electronic device and storage medium

Abstract

The present application provides a method, device, electronic device and storage medium for self-tuning control parameters of an actuator. The method for self-tuning control parameters of an actuator includes: obtaining a response signal of an actuator control system; constructing an objective function according to a given command signal and an obtained response signal, wherein the objective function characterizes the dynamic performance index of the actuator control system, and the optimization variables of the objective function include control parameters; performing Bayesian optimization processing on the objective function to determine the optimal control parameters; and updating the control parameters of the actuator control system according to the optimal control parameters. The technical solution provided by the present application can reduce human intervention while improving the response speed and control accuracy of the actuator control system.

Description

Technical Field

[0001] The present application relates to the technical field of actuator control systems, and in particular to an actuator control parameter self-tuning method, device, electronic equipment and storage medium. Background Art

[0002] More Electric Aircraft (MEA) uses electricity as the main energy source, combined with distributed power electric transmission and electromechanical transmission technology, and the distributed power electric transmission system is mainly composed of electro-hydrostatic actuator (EHA) and electro-mechanical actuator (EMA). Among them, the electro-hydrostatic actuator (EHA) uses a motor-driven hydraulic pump to drive the hydraulic cylinder, which is widely used in the motion control of aircraft control surfaces, ailerons and landing gear. In the actual application environment of the more electric aircraft MEA, the electro-hydrostatic actuator (EHA) system has strong nonlinear characteristics, and thus the adjustment of its dynamic performance and control accuracy becomes a technical difficulty.

[0003] In the related art, the traditional PID control method is used to control the control system of the electric hydrostatic actuator (EHA) to achieve the adjustment of dynamic performance and control accuracy. Among them, most studies mainly provide control parameters in the controller design stage, and perform manual fine-tuning after actual deployment, that is, manually adjust the control parameters, but the manual adjustment process is cumbersome and heavily dependent on experience, and it is difficult to achieve the global optimality of the control parameters. Summary of the invention

[0004] In view of this, the present application provides a method, device, electronic device and storage medium for self-tuning control parameters of an actuator, which can reduce human intervention while improving the response speed and control accuracy of the actuator control system.

[0005] Specifically, the present application is implemented through the following technical solutions:

[0006] The first aspect of the present application provides a method for self-tuning control parameters of an actuator, comprising:

[0007] Acquiring a response signal of an actuator control system;

[0008] Constructing an objective function according to a given command signal and an acquired response signal, wherein the objective function represents a dynamic performance index of the actuator control system, and the optimization variables of the objective function include control parameters;

[0009] Performing Bayesian optimization on the objective function to determine optimal control parameters;

[0010] The control parameters of the actuator control system are updated according to the optimal control parameters.

[0011] In some embodiments, the actuator control system includes a position loop, a speed loop, and a current loop, and obtaining a response signal of the actuator control system includes:

[0012] After determining one or more loops associated with the control parameter from among the position loop, the speed loop, and the current loop, a response signal corresponding to the determined loop is acquired.

[0013] In some embodiments, constructing the target function according to the given command signal and the acquired response signal includes:

[0014] Calculate the tracking error between the given command signal and the acquired response signal;

[0015] The tracking error is integrated with time to establish an error integration criterion ITAE, and the error integration criterion ITAE is used as the objective function.

[0016] In some embodiments, it also includes:

[0017] An initial value of the control parameter is given for performing Bayesian optimization processing on the objective function.

[0018] In some embodiments, performing Bayesian optimization on the objective function to determine the optimal control parameter includes:

[0019] Assuming that the objective function obeys Gaussian distribution, the optimal control parameters are determined by using the set acquisition function expected EI.

[0020] In some embodiments, the setting of the initial value of the control parameter comprises:

[0021] Obtaining an open-loop transfer function of the actuator control system;

[0022] The initial value of the control parameter is determined based on the Ziegler-Nichols law and the response of the open loop transfer function.

[0023] In some embodiments, the method is applied to an electro-hydrostatic actuator (EHA).

[0024] A second aspect of the present application provides an actuator control parameter self-tuning device, comprising:

[0025] a response signal acquisition module, which is configured to acquire a response signal of an actuator control system;

[0026] An objective function construction module, configured to construct an objective function according to a given command signal and an acquired response signal, wherein the objective function represents a dynamic performance index of the actuator control system, and an optimization variable of the objective function includes a control parameter;

[0027] An objective function optimization module, configured to perform Bayesian optimization processing on the objective function to determine optimal control parameters;

[0028] A control parameter updating module is configured to update the control parameters of the actuator control system according to the optimal control parameters.

[0029] A third aspect of the present application provides an electronic device, including:

[0030] Processor; and

[0031] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is caused to execute the method as described above.

[0032] A fourth aspect of the present application provides a computer-readable storage medium having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as described above.

[0033] The technical solution provided by this application has the following beneficial effects:

[0034] First, an objective function that can characterize the dynamic performance index of the actuator control system is constructed through a given command signal and a response signal obtained from the actuator control system, that is, the control parameter setting problem in the actuator control system is first converted into an optimization problem, in which the dynamic performance index of the actuator control system is used as the objective function, and the optimization variables of the objective function are the control parameters, that is, the proportional, integral and differential information of the controller in the actuator; secondly, the objective function is subjected to Bayesian optimization processing to determine the optimal control parameters; finally, the control parameters of the actuator control system are updated according to the optimal control parameters for controlling the actuator control system. Compared with the traditional manual tuning method, the present application can realize the rapid optimization of control parameters in a global range, and has the advantages of faster convergence speed and easier deployment, thereby significantly improving the response speed and control accuracy of the actuator control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is the structural schematic diagram of the control system of the electric hydrostatic actuator EHA;

[0036] Figure 2 A flow chart of a method for self-tuning actuator control parameters provided in an embodiment of the present application;

[0037] Figure 3 is a position trajectory tracking error curve of the position loop in the embodiment of the present application;

[0038] Figure 4 is a speed trajectory tracking error curve of the speed loop in the embodiment of the present application;

[0039] Figure 5 is a current trajectory tracking error curve of the current loop in the embodiment of the present application;

[0040] Figure 6 A schematic diagram of the structure of the actuator control parameter self-tuning device provided in an embodiment of the present application;

[0041] Figure 7 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0043] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0044] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0045] In related technologies, scholars have conducted extensive research on the setting / adjustment of control parameters. In practice, most studies only provide initial control parameters in the controller design stage, and manual fine-tuning of control parameters is still required after actual deployment. In view of the challenges faced by traditional control parameter setting methods, the development of control parameter self-tuning methods that can handle the nonlinear and time-varying characteristics of aviation electric-hydrostatic actuation systems is expected to achieve optimal control performance of the actuation system and reduce manual intervention, thereby improving the reliability of the aircraft actuation system, which has extremely important application value.

[0046] This embodiment provides a method for self-tuning control parameters of an actuator, which can achieve rapid optimization of control parameters in a global range and has the advantages of faster convergence speed and easier deployment, thereby significantly improving the response speed and control accuracy of the actuator control system.

[0047] Specific embodiments are given below to introduce the technical solution of the present application in detail.

[0048] This embodiment provides a method for self-tuning actuator control parameters, which is applied to an electric hydrostatic actuator (EHA). Figure 1 As shown, Figure 1 The schematic diagram of the control system of a common electric hydrostatic actuator EHA is shown in FIG. The control system of the electric hydrostatic actuator EHA is generally divided into a position loop, a speed loop and a current loop from the outside to the inside. After receiving the position command signal given by the outside, the signal difference between the position command signal and the position feedback signal of the EHA control system is the input of the position loop controller, the signal difference between the output of the position loop controller and the speed feedback signal of the EHA control system is the input of the speed loop controller, the signal difference between the output of the speed loop controller and the current feedback signal of the EHA control system is the input of the current loop controller, and the output of the current loop controller is applied to the servo motor through current regulation and power drive. The servo motor is connected to the plunger pump to drive the plunger pump to rotate, and finally drives the external load to move through the oil cylinder. Among them, the control parameters of this embodiment can be the weights of the proportional, integral and differential terms of the PID controllers of the position loop, speed loop and current loop, or the weights of the proportional and integral terms of the PI controller. The specific control parameter combination is selected according to actual needs.

[0049] In the electric hydrostatic actuator EHA, the actuator control parameter self-tuning method can be deployed on the upper computer, and the underlying motion control program of the electric hydrostatic actuator EHA can be deployed on the lower computer to collect the position feedback, speed feedback and current feedback of the electric hydrostatic actuator EHA in real time, that is, the response signals of each loop.

[0050] like Figure 2 As shown, Figure 2 This is a flow chart of a method for self-tuning actuator control parameters provided in an embodiment of the present application. The method mainly includes steps S101 to S104.

[0051] Step S101: Acquire a response signal of an actuator control system.

[0052] Among them, step S101 may further include obtaining a response signal corresponding to the determined loop after determining one or more loops associated with the control parameters from the position loop, speed loop and current loop. In the embodiment of the present application, the response signals corresponding to the position loop, speed loop and current loop are collected in real time, that is, the position information, speed information and current information fed back by the lower computer of the electric hydrostatic actuator EHA to the upper computer. However, not all response signals need to be used in the method, so the upper computer can obtain the required response signal from the lower computer according to the requirements of the actual control parameter combination.

[0053] Step S102: constructing an objective function according to a given command signal and an acquired response signal, wherein the objective function represents a dynamic performance index of the actuator control system, and the optimization variables of the objective function include control parameters.

[0054] In the problem of setting the control parameters, the objective function can be the dynamic performance index of the control system, such as the overshoot of the system response, the steady-state error, the response time, etc., and the optimization variable of the objective function is the control parameter. However, the control parameter is not strictly formed by the combination of all control parameters of the position loop controller, the speed loop controller and the current loop controller, but can also be any one, any two or all of the control parameters according to actual needs. The corresponding controller can be an IP controller or a PID controller.

[0055] In this embodiment, the proportions and integral weights of the PI controllers of the position loop and the speed loop are used as a control parameter combination as the self-tuning control parameters.

[0056] Preferably, step S102 may further include:

[0057] Calculate the tracking error between the given command signal and the acquired response signal;

[0058] The tracking error is integrated with time, the error integration criterion ITAE is established, and the error integration criterion ITAE is used as the objective function.

[0059] In this embodiment, the upper computer obtains the position response signal and speed response signal uploaded by the lower computer in real time, and then calculates the tracking error according to the given command value and response value to construct the objective function to be optimized. The selection of the objective function is closely related to the dynamic performance indicators of the control system. Specifically, the performance indicators for discrete position instructions include response time, steady-state error, overshoot, etc., while the performance indicators for continuously changing position instructions include tracking error and phase difference, etc. In this embodiment, the ITAE criterion is selected to construct the objective function, where the objective function It can be expressed as:

[0060] ;

[0061] In the formula, is the tracking error of a single sampling point, It is the error function between the given signal and the response signal of the position loop and the speed loop.

[0062] Specifically, the control parameters to be optimized are the weights of the proportional term and the integral term of the PI controller of the position loop and the speed loop, so as to find the best PI parameter combination, that is, the best control parameters, by optimizing the objective function.

[0063] Step S103: Perform Bayesian optimization processing on the objective function to determine the optimal control parameters.

[0064] Among them, conventional PID parameter tuning often requires the use of trial and error to determine the parameters, which has limited effect and is time-consuming and labor-intensive. The position loop and speed loop parameters are coupled with each other, making it difficult to obtain the global optimal value. In addition, the optimization problem often involves the problem of finding the extreme value, that is, the problem of finding the derivative. The condition for gradient optimization is that the function form is known and the objective function must be a convex function. The control system of the electric hydrostatic actuator (EHA) usually does not meet this condition, so the gradient optimization method is not applicable. To this end, a Bayesian optimization model based on Gaussian normal distribution is constructed to model the objective function. The range of PI control parameters is given, and the Bayesian optimization algorithm is used to search for the most optimal PI control parameter combination. The Bayesian optimization algorithm gradually approaches the optimal solution by continuously sampling and updating the model.

[0065] As a preferred embodiment of the present application, it also includes:

[0066] The initial values ​​of the control parameters are given for Bayesian optimization of the objective function.

[0067] The initial value of the control parameter may be given by obtaining an open-loop transfer function of the actuator control system and determining the initial value of the control parameter according to the Ziegler-Nichols law and the response of the open-loop transfer function.

[0068] In this embodiment, the system needs to be initialized and set first, and the initial values ​​of the control parameters are given after initialization. If the initial values ​​of the control parameters are not obtained, a step response excitation can be applied to the system first, and the open-loop transfer function of the system can be obtained by the system identification method. Considering the complexity of the actuation system, it is difficult to establish an accurate mathematical model. The initial values ​​of the control parameters are determined by the table lookup method with the help of the response of the open-loop transfer function and the Ziegler-Nichols law. Although the initial values ​​of the control parameters can also be obtained randomly, the randomly obtained initial values ​​may cause the objective function to fail to converge. The given initial values ​​provided by this embodiment can improve the convergence speed of the optimization algorithm, and then improve the response speed of the parameter self-tuning. This embodiment can avoid the direct derivation calculation of the objective function, reduce the sensitivity to the initial value that may lead to the local optimum, and converge faster, which has important practical value.

[0069] Wherein, step S103 may further include:

[0070] Assuming that the objective function obeys Gaussian distribution, the expected EI of the set acquisition function is used to determine the optimal control parameters.

[0071] It should be noted that the prior function and the acquisition function are the core functions of Bayesian optimization, among which the unknown function A set of sampling points , we can get a set of corresponding function values , where the unknown function The objective function is , assuming It follows Gaussian normal distribution and adopts Gaussian process (GP) as the prior function, that is;

[0072] ;

[0073] In the formula, is the mean function of the Gaussian distribution function, is the standard deviation function of the Gaussian distribution function.

[0074] In industrial applications, in order to comply with the prescribed standards and maintain predetermined safety measures, it is necessary to perform online optimization. When looking for the optimal solution of the unknown objective function, the safety constraint S needs to be satisfied to ensure safety, that is;

[0075] ;

[0076] In the formula, represents the feasible region that satisfies the safety constraints.

[0077] According to the prior function, given distribution, we can design an acquisition function to evaluate the position of the next sampling point, and give a satisfactory acquisition function as the expected improvement function (EI), which is expressed as;

[0078] ;

[0079] In the formula, represents the expected expression of the acquisition function, represents the optimal observation point location, represents the probability density function of the normal distribution, A distribution function representing a normal distribution.

[0080] The above acquisition function There is always an optimal point x opt , so that;

[0081] ;

[0082] In order to strike a balance between maximizing the objective function value and minimizing uncertainty, x can be selected i+1 As the optimal control parameter, .

[0083] Specifically, according to the Bayesian optimization model, after a set of PI control parameters is given, the response signal of the actuator system is obtained. According to the objective function constructed by the given signal and the response signal, the next set of PI control parameters to be sampled is predicted and generated. The iteration is repeated until a certain number of iterations is reached or the stop condition is met, and the optimal PI control parameters are finally obtained. The core problem of control parameter self-tuning is how to select the next set of control parameter vectors to be verified in each iteration. , so that , assuming that the iterative cycle is traversed i times , a set of control parameters corresponding to the entire range The value follows a one-dimensional Gaussian distribution, that is, the model is assumed It obeys Gaussian normal distribution and uses Gaussian process (GP) as the prior function, that is;

[0084] ;

[0085] In the formula, is the mean function of the Gaussian distribution function, is the standard deviation function of the Gaussian distribution function.

[0086] Can be defined , that is to say, there exists a set of control parameters within the interval that can minimize the objective function value and meet the performance index requirements, where the expected expression of the acquisition function is defined as;

[0087] ;

[0088] In the formula, represents the expected expression of the acquisition function, represents the optimal observation point location, represents the probability density function of the normal distribution, A distribution function representing a normal distribution.

[0089] Among them, the next observed control parameter vector can be expressed as;

[0090] ;

[0091] There is always an optimal control parameter vector x opt , so that;

[0092] ;

[0093] x i+1 As the optimal control parameter, .

[0094] Step S104: updating the control parameters of the actuator control system according to the optimal control parameters.

[0095] In this embodiment, after determining the optimal control parameters, the upper computer sends the optimal control parameters to the lower computer, so that the lower computer uses the optimal control parameters to control the actuator system. In addition, the upper computer also calls the lower computer online to obtain a response signal, thereby dynamically adjusting the actuator control parameters, that is, completing the self-tuning of the control parameters.

[0096] Verification, considering the complexity of the EHA system, the EHA system has a relatively low frequency response, and the position loop, velocity loop and current loop in the system are all controlled by PI controllers to achieve accurate and fast control of the hydraulic cylinder position. An aviation EHA simulation model is constructed in the MATLAB / Simulink simulation environment, the simulation time is set to 5s, and a sudden load with an amplitude of 40kN is applied at 3.25s. The manual adjustment of the PI control parameters is first estimated by the Ziegler-Nichols method, and the optimal control parameters are obtained through repeated experiments, and the traditional relay feedback method is selected as the control group. Compared with the manual adjustment control parameter method Manual, the relay feedback method RF and the method Bayes provided in the embodiment of the present application both show faster response speed and smaller steady-state tracking error.

[0097] Figure 3 , Figure 4 and Figure 5These correspond in turn to the position trajectory tracking error curve of the position loop, the speed trajectory tracking error curve of the speed loop, and the current trajectory tracking error curve of the current loop.

[0098] Specifically, from Figure 3 It can be seen that the EHA system has a large tracking error after experiencing a sudden load. When the method provided in the embodiment of the present application is used, the maximum steady-state error is reduced by 21.39% and the root mean square error is reduced by 50.04%. It can be seen that the use of self-tuning control parameters can make the EHA system show higher robustness, and make the EHA system have a smaller impact error and converge to the steady-state tracking error faster.

[0099] from Figure 4 and Figure 5 It can be seen that compared with the simulation results of the manually adjusted control parameters, the relay feedback method shows obvious oscillation due to the strong nonlinearity, and the method provided in the embodiment of the present application is superior to the manually adjusted control parameters in both the speed loop and the current loop, and the maximum tracking error is reduced by 9.03% and 21.05%, respectively, and the root mean square error is reduced by 0.26% and 16.92%, respectively. It can be seen that the method provided in the embodiment of the present application is also significantly superior to the relay feedback method.

[0100] In summary, compared with the traditional manual tuning method and relay feedback method, the simulation results show that the method provided by the embodiment of the present application significantly improves the response speed and control accuracy of the system. Specifically, the maximum tracking errors in the position loop, speed loop, and current loop are reduced by 21.39%, 9.03%, and 21.05%, respectively, while the root mean square error is reduced by 50.04%, 0.26%, and 16.92%, respectively. These results prove the advancement and robustness of the method proposed in the embodiment of the present application.

[0101] Corresponding to the aforementioned embodiment of a method for self-tuning control parameters of an actuator, the present application also provides an embodiment of a device for self-tuning control parameters of an actuator.

[0102] Figure 6 A schematic diagram of the structure of the actuator control parameter self-tuning device provided in an embodiment of the present application.

[0103] like Figure 6 As shown, the actuator control parameter self-tuning device provided in this embodiment is applied to an electric hydrostatic actuator (EHA), and includes:

[0104] A response signal acquisition module 601, which is configured to acquire a response signal of an actuator control system;

[0105] An objective function construction module 602 is configured to construct an objective function according to a given command signal and an acquired response signal, wherein the objective function represents a dynamic performance index of the actuator control system, and an optimization variable of the objective function includes a control parameter;

[0106] An objective function optimization module 603, which is configured to perform Bayesian optimization processing on the objective function to determine the optimal control parameters;

[0107] The control parameter updating module 604 is configured to update the control parameters of the actuator control system according to the optimal control parameters.

[0108] It should be noted that the device of this embodiment can be used to perform Figure 2 The steps, specific implementation principles and implementation processes of the method embodiment shown are similar and will not be repeated here.

[0109] Furthermore, the actuator control system includes a position loop, a speed loop and a current loop, and the response signal acquisition module 601 can be further configured to acquire a response signal corresponding to the determined loop after determining one or more loops associated with the control parameter from the position loop, the speed loop and the current loop.

[0110] Furthermore, the objective function construction module 602 can be further configured to calculate the tracking error of the given command signal and the acquired response signal; integrate the tracking error with time, establish the error integration criterion ITAE, and use the error integration criterion ITAE as the objective function.

[0111] Preferably, the device further comprises a control parameter setting module, which can be configured to set an initial value of the control parameter for performing Bayesian optimization processing on the objective function.

[0112] Among them, the control parameter setting module may include an acquisition unit and a determination unit, the acquisition unit may be further configured to acquire the open-loop transfer function of the actuator control system, and the determination unit may be further configured to determine the initial value of the control parameter according to the Ziegler-Nichols law and the response of the open-loop transfer function.

[0113] Furthermore, the objective function optimization module 603 may be further configured to assume that the objective function obeys a Gaussian distribution and adopt a set acquisition function expected EI to determine the optimal control parameter.

[0114] Regarding the device in the above embodiment, the implementation process of the functions and effects of each unit therein is specifically described in the implementation process of the corresponding steps in the above method, and will not be elaborated on in detail here.

[0115] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0116] Figure 7 It is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application.

[0117] See also Figure 7 , the electronic device 700 includes a memory 701 and a processor 702.

[0118] The processor 702 may 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. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0119] The memory 701 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. Among them, ROM can store static data or instructions required by the processor 702 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, an optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory 701 may include any combination of computer-readable storage media, including various types of semiconductor storage chips (such as DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 701 may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (such as a DVD-ROM, a double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a mini SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0120] The memory 701 stores executable codes, and when the executable codes are processed by the processor 702 , the processor 702 can execute part or all of the methods described above.

[0121] Among them, the above-mentioned electronic equipment can be a more electric aircraft.

[0122] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0123] Alternatively, the present application can also be implemented as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) on which executable code (or computer program or computer instruction code) is stored. When the executable code (or computer program or computer instruction code) is executed by a processor of an electronic device (or server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0124] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for self-tuning control parameters of an actuator, characterized in that: include: Acquiring a response signal of the actuator control system, wherein one or more loops associated with the actuator control parameters are determined from the position loop, the speed loop, and the current loop, and acquiring a response signal corresponding to the determined loop; Constructing an objective function according to a given command signal and an acquired response signal, wherein the objective function represents a dynamic performance index of the actuator control system, and the optimization variables of the objective function include control parameters; The objective function is subjected to Bayesian optimization processing to determine the optimal control parameter, wherein an initial value of the control parameter is given for the Bayesian optimization processing of the objective function; if the initial value of the control parameter is not obtained, a step response excitation is applied to the actuator, and an open-loop transfer function of the actuator is obtained by a system identification method; and the initial value of the control parameter is determined based on the response of the open-loop transfer function and the Ziegler-Nichols law; The control parameters of the actuator control system are updated according to the optimal control parameters.

2. The actuator control parameter self-tuning method according to claim 1, characterized in that: The step of constructing an objective function according to a given command signal and an acquired response signal comprises: Calculate the tracking error between the given command signal and the acquired response signal; The tracking error is integrated with time to establish an error integration criterion ITAE, and the error integration criterion ITAE is used as the objective function.

3. The actuator control parameter self-tuning method according to claim 1, characterized in that: The performing Bayesian optimization processing on the objective function to determine the optimal control parameters includes: Assuming that the objective function obeys Gaussian distribution, the optimal control parameters are determined by using the set acquisition function expected EI.

4. The method for self-tuning actuator control parameters according to any one of claims 1 to 3, characterized in that: The actuator control parameter self-tuning method is applied to an electric hydrostatic actuator (EHA).

5. An actuator control parameter self-tuning device, characterized in that: include: A response signal acquisition module is configured to acquire a response signal of the actuator control system, wherein one or more loops associated with the actuator control parameters are determined from the position loop, the speed loop and the current loop, and a response signal corresponding to the determined loop is acquired; An objective function construction module, configured to construct an objective function according to a given command signal and an acquired response signal, wherein the objective function represents a dynamic performance index of the actuator control system, and an optimization variable of the objective function includes a control parameter; an objective function optimization module, configured to perform Bayesian optimization processing on the objective function to determine the optimal control parameter, wherein an initial value of the control parameter is given for the Bayesian optimization processing of the objective function; if the initial value of the control parameter is not obtained, a step response excitation is applied to the actuator, and an open-loop transfer function of the actuator is obtained by a system identification method; and the initial value of the control parameter is determined based on the response of the open-loop transfer function and the Ziegler-Nichols law; A control parameter updating module is configured to update the control parameters of the actuator control system according to the optimal control parameters.

6. An electronic device, characterized in that: include: processor; as well as A memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that: An executable code is stored thereon, and when the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as claimed in any one of claims 1 to 5.

Images