Robust controller management method and device, equipment and storage medium

Abstract

The invention relates to the technical field of motor design, and discloses a management method, device and equipment of a robust controller and a storage medium, the management method of the robust controller is applied to a permanent magnet synchronous motor, and the method comprises the following steps: obtaining a weighting function structure of the robust controller; optimizing parameters in the weighting function structure according to an improved particle swarm optimization algorithm; obtaining a weighting function according to the parameters and the weighting function structure; and solving the robust controller through the weighting function. And a corresponding robust controller is accurately and quickly designed by using an improved particle swarm optimization algorithm. A robust controller is used for replacing traditional PI control, and external interference and internal interference of a control system can be considered. Under the condition that motor parameters change and the model is uncertain, robust control over the permanent magnet synchronous motor is achieved, system buffeting is prevented, and the control precision is improved.

Description

Technical Field

[0001] The present application relates to the technical field of motor design, and in particular to a management method, device, equipment and storage medium of a robust controller. Background Art

[0002] Robust control is an advanced technology that can be used for motor control. It aims to achieve strong robust stability and performance robustness for motor systems. This control technology is often used in fields that require precise control of motor systems and require system stability, such as aerospace, automotive industry, and industrial automation. H∞ robust control is used to solve the impact of process parameter changes, external interference and other factors on control performance. The selection of weighting function can be said to be the most important part of robust control design, because the weighting function reflects the stability of the control system itself and other system performance indicators. In actual application, it needs to be continuously adjusted according to the characteristics of the controlled object itself and the control performance to be achieved. At present, there is no set of standard solution formulas available for application.

[0003] Some scholars calculate the weighted function through some optimization algorithms. However, due to the problems of slow convergence and easy falling into local optimality of traditional optimization algorithms, the performance of the robust controller finally designed is relatively low. Summary of the invention

[0004] The purpose of this application is to provide a management method, device, equipment and storage medium for a robust controller, aiming to solve the technical problem of low control accuracy of a robust controller in a permanent magnet synchronous motor control system.

[0005] To achieve the above object, the present application proposes a management method of a robust controller, the management method of the robust controller is applied to a permanent magnet synchronous motor, and the method comprises:

[0006] Obtaining the weighting function structure of the robust controller;

[0007] Optimizing the parameters in the weighted function structure according to an improved particle swarm optimization algorithm;

[0008] Acquire a weighting function according to the parameters and the weighting function structure;

[0009] The robust controller is solved by the weighting function.

[0010] In one embodiment, the step of obtaining the weighted function structure of the robust controller includes:

[0011] Obtaining a first function structure, a second function structure and unknown constants;

[0012] The weighted function structure of the robust controller is composed according to the first function structure, the second function structure and the unknown constant.

[0013] In one embodiment, before the step of optimizing the parameters in the weighted function structure according to the improved particle swarm optimization algorithm, the following steps are included:

[0014] Obtaining a first parameter, a second parameter, and a third parameter in the first function structure;

[0015] Obtaining the fourth parameter, the fifth parameter and the sixth parameter in the second function structure;

[0016] The first parameter, the second parameter, the third parameter, the fourth parameter, the fifth parameter, the sixth parameter and the unknown constant are used as parameters in the weighting function structure.

[0017] In one embodiment, the step of optimizing the parameters in the weighted function structure according to the improved particle swarm optimization algorithm includes:

[0018] Initialize and improve IPSO population;

[0019] The optimal values ​​of the parameters in the weighted function structure are determined by performing optimization through IPSO.

[0020] In one embodiment, the step of acquiring the weighting function according to the parameters and the weighting function structure includes:

[0021] Substituting the optimal value into the first function structure, the second function structure and the unknown constant to obtain a first function, a second function and an optimal constant respectively;

[0022] The weighting function is composed according to the first function, the second function and the optimal constant.

[0023] In one embodiment, before the step of solving the robust controller by using the weighted function, the following steps are included:

[0024] Determining singular values ​​of the first function and singular values ​​of the second function;

[0025] When the singular value of the first function and the singular value of the second function are both lower than a first preset value, performing the step of solving the robust controller by using the weighted function;

[0026] When the singular value of the first function or the second function is not lower than the first preset value, return to the step of performing optimization through IPSO to determine the optimal values ​​of the parameters in the weighted function structure.

[0027] In one embodiment, after the step of solving the robust controller by using the weighted function, the following steps are included:

[0028] Simulating the robust controller and obtaining a simulation result graph;

[0029] Comparing the tracking performance of the curves in the simulation result graph and the traditional control simulation graph;

[0030] When the tracking performance of the curve in the simulation result diagram is higher than the tracking performance of the curve in the traditional control simulation diagram, selecting the robust controller to be applied to the permanent magnet synchronous motor;

[0031] When the tracking performance of the curve in the simulation result graph is not higher than the tracking performance of the curve in the traditional control simulation graph, return to the step of optimizing the parameters in the weighted function structure according to the improved particle swarm optimization algorithm.

[0032] In addition, to achieve the above objectives, the present application also proposes a management device for a robust controller, the device comprising:

[0033] A structure acquisition module, used to obtain the weighted function structure of the robust controller;

[0034] A parameter optimization module, used for optimizing the parameters in the weighted function structure according to an improved particle swarm optimization algorithm;

[0035] A function acquisition module, used for acquiring a weighting function according to the parameters and the weighting function structure;

[0036] The target solving module is used to solve the robust controller through the weighting function.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a management device for a robust controller, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the management method for the robust controller as described above.

[0038] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the management method of the robust controller as described above are implemented.

[0039] The present application proposes a management method for a robust controller, which is applied to a permanent magnet synchronous motor. The method includes: obtaining a weighted function structure of a robust controller; optimizing the parameters in the weighted function structure according to an improved particle swarm optimization algorithm; obtaining a weighted function according to the parameters and the weighted function structure; and solving a robust controller through the weighted function. By using the robust control design method of a permanent magnet synchronous motor optimized by an improved particle swarm algorithm, a weighted function containing 6 parameters can be designed to reduce the controller order after calculating the corresponding nominal function for the permanent magnet synchronous motor as the controlled object, so as to improve the optimization speed, improve the optimization accuracy, and prevent local optimality. The corresponding robust controller is accurately and quickly designed using the improved particle swarm optimization algorithm. Using a robust controller instead of traditional PI control can take into account external and internal interference of the control system. In the case of changes in motor parameters and model uncertainty, robust control of the permanent magnet synchronous motor is achieved, system chattering is prevented, and control accuracy is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0042] Figure 1 A flowchart diagram of a management method for a robust controller according to Embodiment 1 of the present application;

[0043] Figure 2 A flowchart diagram of a second embodiment of a management method for a robust controller of the present application;

[0044] Figure 3 A flowchart diagram of a third embodiment of the management method of a robust controller of the present application;

[0045] Figure 4 A schematic diagram of the module structure of a management device of a robust controller according to an embodiment of the present application;

[0046] Figure 5 A schematic diagram of the structure of a management device of a robust controller in a hardware operating environment involved in a management method of a robust controller in an embodiment of the present application.

[0047] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0048] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0049] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0050] The main solution of the embodiment of the present application is: obtain a weighted function structure of a robust controller; optimize the parameters in the weighted function structure according to an improved particle swarm optimization algorithm; obtain a weighted function according to the parameters and the weighted function structure; and solve the robust controller through the weighted function.

[0051] At present, some scholars use some optimization algorithms to calculate the weighted function. However, due to the problems of slow convergence speed and easy falling into local optimality of traditional optimization algorithms, the performance of the robust controller finally designed is relatively low.

[0052] The present application provides a solution. By using the improved particle swarm algorithm to optimize the robust control design method of permanent magnet synchronous motor, the permanent magnet synchronous motor can be used as the controlled object. After the corresponding nominal function is calculated, a weighted function containing 6 parameters is designed to reduce the controller order, in order to increase the optimization speed, improve the optimization accuracy, and prevent local optimality. The corresponding robust controller is accurately and quickly designed using the improved particle swarm optimization algorithm. Using a robust controller instead of traditional PI control can take into account external and internal interference of the control system. In the case of changes in motor parameters and model uncertainty, the robust control of the permanent magnet synchronous motor is achieved, system chattering is prevented, and control accuracy is improved.

[0053] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a management device of a robust controller capable of realizing the above functions, etc. The following takes the management device of a robust controller as an example to illustrate this embodiment and the following embodiments.

[0054] Based on this, the present application embodiment provides a management method for a robust controller, referring to Figure 1 , Figure 1 A flowchart diagram is provided for the first embodiment of the management method of the robust controller of the present application.

[0055] In this embodiment, the management method of the robust controller includes steps S10 to S40:

[0056] Step S10: Obtain the weighted function structure of the robust controller.

[0057] It should be noted that the design of the robust controller aims to enable the control system to maintain good performance in the presence of uncertainties (such as model errors, external disturbances, etc.). The determination of the weighted function structure is usually based on the analysis of the performance requirements of the control system. For example, for a system with disturbance suppression requirements, there may be a weighted function structure for disturbance input. It may be in the form of a fraction, such as W(s) = N(s) / D(s), where N(s) and D(s) are polynomials about the complex variable s. The determination of this structure needs to consider factors such as the dynamic characteristics of the system and the desired robust performance indicators.

[0058] Step S20: Optimizing the parameters in the weighted function structure according to the improved particle swarm optimization algorithm.

[0059] It should be understood that the particle swarm optimization algorithm (PSO) is an optimization algorithm based on swarm intelligence. The improved particle swarm optimization algorithm may improve the particle update rules, inertia weights, etc. on the basis of the original algorithm. After the weighting function structure is determined, it will contain some parameters, such as the coefficients of the polynomials N(s) and D(s). These parameters will affect the characteristics of the weighting function and thus affect the performance of the robust controller. Using the improved particle swarm optimization algorithm, these parameters are used as optimization variables. Each particle in the algorithm represents a set of parameter values. According to a predefined fitness function (such as a function based on robust stability and performance indicators), particles continuously update their positions in the search space (i.e., update parameter values) to find the parameter combination that optimizes the fitness function.

[0060] Step S30: Obtain a weighting function according to the parameters and the weighting function structure.

[0061] It should be understood that by improving the particle swarm optimization algorithm to find the optimal parameter values, these parameters are substituted into the weighted function structure to obtain a specific weighted function. For example, if the weighted function structure is W(s) = (a0+a1s+a2s 2 ) / (b0+b1s+b2s 2 ), through optimization, we get a0=1, a1=2, a2=3, b0=4, b1=5, b2=6, then the weighting function W(s)=(1+2s+3s 2 ) / (4+5s+6s 2 ).

[0062] Step S40: solving the robust controller by using the weighting function.

[0063] With the weighting function, the relevant methods in robust control theory can be used to solve the robust controller. For example, in H∞ robust control, the expression of the robust controller can be obtained by solving specific matrix inequalities (such as Riccati inequality, etc.). In this process, the weighting function plays the role of adjusting the response weight of the control system to different inputs (such as reference input, disturbance input, etc.), so that the designed robust controller can meet the stability and performance requirements in the presence of uncertainty.

[0064] This embodiment proposes a management method for a robust controller, which is applied to a permanent magnet synchronous motor, and the method includes: obtaining a weighted function structure of the robust controller; optimizing the parameters in the weighted function structure according to an improved particle swarm optimization algorithm; obtaining a weighted function according to the parameters and the weighted function structure; and solving the robust controller through the weighted function. Using a robust controller instead of traditional PI control can take into account external and internal interference of the control system. In the case of changes in motor parameters and model uncertainty, robust control of the permanent magnet synchronous motor is achieved, system jitter is prevented, and control accuracy is improved.

[0065] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 , Figure 2 A flowchart of the second embodiment of the management method of the robust controller of the present application is provided. In step S10, the management method of the robust controller further includes:

[0066] Step S110: Obtain the first function structure, the second function structure and unknown constants;

[0067] Step S120: forming the weighted function structure of the robust controller according to the first function structure, the second function structure and the unknown constant.

[0068] It should be understood that in the standard H∞ robust control system, the nominal model of the permanent magnet synchronous motor is assumed to be P(s), and three weighted functions are selected and represented by W1, W2, and W3 respectively. Now define:

[0069]

[0070] Among them, S is the transfer function matrix from the error signal e to the input signal r, and the transfer function matrix that can represent d to the output signal y is called the sensitivity function; T is the transfer function from the system output y to the system input r, called the complementary sensitivity function; R is a measure of the magnitude of the additive uncertainty, called the input sensitivity function. Among them, the infinite norm of the sensitivity function and the complementary sensitivity function represents the impact of interference on the system, requiring S and T to be as small as possible. Since S+T=I is required in the same frequency band, an appropriate weighting function is required for compromise selection. The appropriate weighting function will directly affect the stability and robustness of the system. Therefore, finding a weighting function that meets the performance of the control system has always been a hot topic and focus of robust control research.

[0071] Before step S20, the management method of the robust controller further includes:

[0072] Step S130: obtaining a first parameter, a second parameter and a third parameter in the first function structure;

[0073] Step S140: obtaining the fourth parameter, the fifth parameter and the sixth parameter in the second function structure;

[0074] Step S150: taking the first parameter, the second parameter, the third parameter, the fourth parameter, the fifth parameter, the sixth parameter and the unknown constant as parameters in the weighting function structure.

[0075] It should be noted that, considering the dynamic characteristics of the control system, the uncertainty and disturbance of the system can be effectively suppressed and the controller order can be reduced as much as possible to ensure the feasibility of hardware implementation. In the present invention, W1 and W3 are selected as first-order functions to reduce the number of unknown parameters, and W2 is set to a small constant, such as 0.001. Design a robust controller full parameter weighted function structure: W2(S)=0.001, By determining the six parameters A (first parameter), B (second parameter), C (third parameter), D (fourth parameter), E (fifth parameter), and F (sixth parameter), three weighted functions can be designed, and then a robust controller that meets the conditions can be calculated. This paper will improve the particle swarm algorithm, optimize the six parameters, find the optimal weighted function that meets the conditions, and then calculate a robust controller that meets the conditions.

[0076] In step S20, the management method of the robust controller further includes:

[0077] Step S210: Initialize the improved IPSO population;

[0078] Step S220: performing optimization through IPSO to determine the optimal values ​​of the parameters in the weighted function structure.

[0079] It should be noted that the traditional particle swarm algorithm generally uses random initialization to determine the position distribution of the initial population, and randomly generates the initial position of each particle according to the following formula.

[0080] Positions=Rand*(Ub-Lb)+Lb (2)

[0081] Positions represents the position of the generated particles; Rand represents the generated random number, with a value range of [0,1]; Ub and Lb are the upper and lower bounds of the solution space, respectively. The above formula can be used to quickly and easily initialize the particle swarm, and it can also ensure that the initialization values ​​are different each time. Due to its randomness, the positions of the initialized particle population are often unevenly distributed, and the initial density of particles in different regions is different, resulting in limited search space in the early iterations, which is easy to fall into the local optimum, and will also cause the convergence speed to decrease or even fail to converge.

[0082] Initialize the particle population with the mean value, and divide the particle value interval equally when the particles determine the upper and lower limits of the parameters.

[0083]

[0084] Among them, Positions2 represents the particle position spacing. The particle population initialized by the mean is evenly distributed in the entire space, which can ensure sufficient search space in the initial iteration stage and prevent early entry into the local optimum.

[0085] It should be understood that the traditional fixed learning factor remains unchanged throughout the particle swarm optimization process. In the early stage of the algorithm, if the cognitive factor (the weight of the particle's own experience) and the social factor (the weight of the group's optimal experience) are set improperly, the particles may not be able to fully explore the entire search space. For complex high-dimensional optimization problems, fixed learning factors may lead to low search efficiency. Since the learning factor cannot be adjusted dynamically, the particle swarm may not be able to fine-tune the search behavior when it is close to the optimal solution. In the later stage of the search, it is ideal to increase the social factor appropriately so that the particles can better use the group's optimal information for local search and improve the convergence accuracy. Based on the linear dynamic learning factor, since its learning factor rises and falls at a fixed slope, although the learning factor is dynamically adjusted to a certain extent, it is impossible to quickly adjust the learning factor in the early and late stages. For highly complex multi-modal functions or high-dimensional optimization problems, the linear dynamic learning factor may not be able to effectively balance global search and local search. In multi-modal function optimization, particles need to flexibly switch exploration and development strategies between different peak areas.

[0086] It should be noted that c1 and c2 are learning factors, and their values ​​affect the individual learning and population learning of the particle population. In the traditional particle swarm algorithm, c1 and c2 are generally set to a fixed value of 1.49445, which solidifies the learning factor of the particle and makes it impossible to dynamically adjust its own learning parameters according to the iteration. In order to prevent the particles from being densely distributed locally in the early stage of the iteration and dispersed in the later stage of the iteration, this paper constructs c1 as a monotonically decreasing function and c2 as a monotonically increasing function to ensure that c1 is larger and c2 is smaller in the early stage; the opposite is true in the later stage. The expression is as follows:

[0087]

[0088] Where K is the number of iterations, and Maxgen is the maximum number of iterations. By using the dynamic learning factor to improve the particle swarm optimization algorithm, the group can quickly search for the optimal value in a short time in the early stage of evolution, and can quickly and accurately converge to the optimal solution in the later stage of evolution.

[0089] It should be understood that the genetic algorithm seeks the optimal solution by simulating natural selection and genetic mechanisms. It starts with an initial population, and each individual represents a potential solution to the problem. In each generation, the fitness function is used to evaluate the quality of the individuals, and individuals with high fitness are more likely to be selected for reproduction. The reproduction process includes crossover and mutation operations. The crossover operation combines some features of two parent individuals to generate new individuals, and the mutation operation randomly changes certain features of individuals to increase the diversity of the population. After multiple generations of evolution, the individuals in the population gradually approach the optimal solution. Due to its disadvantages such as large amount of calculation, slow convergence speed, and difficulty in parameter selection, this paper improves the advantages of the particle swarm algorithm by mixing and improving the particle swarm algorithm. The particle swarm algorithm calculates the fitness of the particle population and then sorts it, removes the part with low fitness, selects the one with good fitness as the hybrid pool, selects the particles that need to be hybridized and replaces the parent with the offspring, and then selects the particles to be mutated for Gaussian mutation, and finally finds the most suitable particle swarm.

[0090] It should be noted that, in step S30, the management method of the robust controller also includes: substituting the optimal value into the first function structure, the second function structure and the unknown constant to obtain the first function, the second function and the optimal constant respectively; and forming the weighted function according to the first function, the second function and the optimal constant.

[0091] In this embodiment, by using the improved particle swarm optimization algorithm to optimize the robust control design method of the permanent magnet synchronous motor, the permanent magnet synchronous motor can be used as the controlled object. After the corresponding nominal function is calculated, a weighted function containing 6 parameters is designed to reduce the controller order, in order to improve the optimization speed, improve the optimization accuracy, and prevent local optimal. The improved particle swarm optimization algorithm is used to accurately and quickly design the corresponding robust controller. The robust control of the permanent magnet synchronous motor is realized, the system chattering is prevented, and the control accuracy is improved.

[0092] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those of the above-mentioned first and second embodiments can refer to the above introduction, and will not be repeated later. Figure 3 , Figure 3 A flowchart of the third embodiment of the management method of the robust controller of the present application is provided. In step S20, the management method of the robust controller further includes:

[0093] Before step S40, the management method of the robust controller further includes:

[0094] Step S410: determining the singular values ​​of the first function and the singular values ​​of the second function.

[0095] It should be noted that in mathematics, singular values ​​are an important feature of matrices. For a matrix A, its singular value decomposition A = UΣV T , where Σ is a diagonal matrix, and the elements on the diagonal are the singular values ​​σ i . Singular values ​​reflect many properties of the matrix. For example, the rank of the matrix can be determined by the number of non-zero singular values.

[0096] Step S420: When the singular value of the first function and the singular value of the second function are both lower than a first preset value, performing the step of solving the robust controller by using the weighted function.

[0097] It should be noted that if the singular value of the first function and the singular value of the second function are both lower than the first preset value, then the step of solving the robust controller by the weighted function is performed. This means that in this case, some properties related to the singular values ​​of the two functions meet the conditions for solving the robust controller.

[0098] Step S430: when the singular value of the first function or the second function is not lower than the first preset value, return to the step of performing optimization through IPSO to determine the optimal values ​​of the parameters in the weighted function structure.

[0099] It should be understood that when the singular value of the first function or the second function is not lower than the first preset value, it is necessary to return to the step of determining the optimal value of the parameter in the weighted function structure through IPSO optimization. This indicates that as long as the singular value of one of the functions does not meet the requirements, it is necessary to readjust the parameters in the weighted function structure to meet the requirements of subsequent operations (such as solving the robust controller) for the properties related to the singular value.

[0100] After step S40, the management method of the robust controller further includes:

[0101] Step S440: simulating the robust controller and obtaining a simulation result graph;

[0102] Step S450: comparing the tracking performance of the simulation result graph with the curve in the traditional control simulation graph;

[0103] Step S460: When the tracking performance of the curve in the simulation result graph is higher than the tracking performance of the curve in the traditional control simulation graph, selecting the robust controller to be applied to the permanent magnet synchronous motor;

[0104] Step S470: When the tracking performance of the curve in the simulation result graph is not higher than the tracking performance of the curve in the traditional control simulation graph, return to the step of optimizing the parameters in the weighted function structure according to the improved particle swarm optimization algorithm.

[0105] It should be understood that, considering the dynamic characteristics of the control system, the uncertainty and disturbance of the system can be effectively suppressed and the controller order can be reduced as much as possible to ensure the feasibility of hardware implementation. In this paper, W1(s) and W3(s) are selected as first-order functions to reduce the number of unknown parameters. In order to traverse all weighted functions that meet the conditions in the optimization, the numerator and denominator parameters to be measured in W1(s) and W3(s) are taken into account, and W2(s) is set to a smaller constant. Design a robust controller with full parameter weighted function structure. Refer to Example 2. By determining the six parameters A, B, C, D, E, and F, three weighted functions can be designed, and then a robust controller that meets the conditions can be calculated. This paper will optimize the six parameters by improving the particle swarm algorithm to find the optimal weighted function that meets the conditions.

[0106] It should be understood that after designing the improved particle swarm optimization algorithm, this paper sets the fitness function to f1 = min K ‖T zw ‖ ∞ , Where T zw Represents the transfer function from w to z: T zw (s) = G 11 +G 12 K[IG 22 K(s)] -1 G21 ; e(t) is the speed error in the Simulink simulation model. Limit function:

[0107]

[0108] In this embodiment, by using the improved particle swarm optimization algorithm to optimize the robust control design method of the permanent magnet synchronous motor, the permanent magnet synchronous motor can be used as the controlled object. After the corresponding nominal function is calculated, a weighted function containing 6 parameters is designed to reduce the controller order, in order to improve the optimization speed, improve the optimization accuracy, and prevent local optimality. The improved particle swarm optimization algorithm is used to accurately and quickly design the corresponding robust controller. Using a robust controller instead of traditional PI control can take into account the external and internal interference of the control system. In the case of changes in motor parameters and model uncertainty, the robust control of the permanent magnet synchronous motor is realized, the system chattering is prevented, and the control accuracy is improved.

[0109] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the management method of the robust controller of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0110] This application also provides a management device for a robust controller, please refer to Figure 4 , the management device of the robust controller comprises:

[0111] A structure acquisition module 10, used to acquire a weighted function structure of a robust controller;

[0112] A parameter optimization module 20, used for optimizing the parameters in the weighted function structure according to an improved particle swarm optimization algorithm;

[0113] A function acquisition module 30, used to acquire a weighting function according to the parameters and the weighting function structure;

[0114] The target solving module 40 is used to solve the robust controller through the weighting function.

[0115] Optionally, the structure acquisition module 10 is also used to acquire a first function structure, a second function structure and unknown constants; and to form the weighted function structure of the robust controller according to the first function structure, the second function structure and the unknown constants.

[0116] Optionally, the structure acquisition module 10 is also used to obtain the first parameter, the second parameter and the third parameter in the first function structure; obtain the fourth parameter, the fifth parameter and the sixth parameter in the second function structure; and use the first parameter, the second parameter, the third parameter, the fourth parameter, the fifth parameter, the sixth parameter and the unknown constant as parameters in the weighted function structure.

[0117] Optionally, the structure acquisition module 10 is further used to initialize an improved IPSO population; and to determine the optimal values ​​of the parameters in the weighted function structure by performing optimization through IPSO.

[0118] Optionally, the structure acquisition module 10 is also used to substitute the optimal value into the first function structure, the second function structure and the unknown constant to obtain the first function, the second function and the optimal constant respectively; and form the weighted function according to the first function, the second function and the optimal constant.

[0119] Optionally, the structure acquisition module 10 is also used to determine the singular value of the first function and the singular value of the second function; when the singular value of the first function and the singular value of the second function are both lower than a first preset value, the step of solving the robust controller through the weighted function is performed; when the singular value of the first function or the second function is not lower than the first preset value, the step of determining the optimal value of the parameters in the weighted function structure through optimization through IPSO is returned.

[0120] Optionally, the target solving module 40 is also used to simulate the robust controller and obtain a simulation result graph; compare the tracking performance of the simulation result graph with that of the curve in the traditional control simulation graph; when the tracking performance of the curve in the simulation result graph is higher than that of the curve in the traditional control simulation graph, select the robust controller to be applied to the permanent magnet synchronous motor; when the tracking performance of the curve in the simulation result graph is not higher than that of the curve in the traditional control simulation graph, return to the step of optimizing the parameters in the weighted function structure according to the improved particle swarm optimization algorithm.

[0121] The management device of the robust controller provided in the present application adopts the management method of the robust controller in the above embodiment, which can solve the technical problem of low control accuracy of the robust controller in the permanent magnet synchronous motor control system. Compared with the prior art, the beneficial effects of the management device of the robust controller provided in the present application are the same as the beneficial effects of the management method of the robust controller provided in the above embodiment, and other technical features in the management device of the robust controller are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0122] The present application provides a management device for a robust controller, the management device for the robust controller comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the management method for the robust controller in the above-mentioned embodiment one.

[0123] Reference below Figure 5, which shows a schematic diagram of the structure of a management device of a robust controller suitable for implementing an embodiment of the present application. The management device of the robust controller in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The management device of the robust controller shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0124] like Figure 5 As shown, the management device of the robust controller may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for device operation are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the management device of the robust controller to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows the management device of the robust controller with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0125] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0126] The management device of the robust controller provided in the present application adopts the management method of the robust controller in the above embodiment, which can solve the technical problem of low control accuracy of the robust controller in the permanent magnet synchronous motor control system. Compared with the prior art, the beneficial effects of the management device of the robust controller provided in the present application are the same as the beneficial effects of the management method of the robust controller provided in the above embodiment, and other technical features in the management device of the robust controller are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0127] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0128] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0129] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the management method of the robust controller in the above-mentioned embodiment.

[0130] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0131] The computer-readable storage medium may be included in the management device of the robust controller; or may exist independently without being assembled into the management device of the robust controller.

[0132] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0133] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0134] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0135] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the management method of the above-mentioned robust controller, and can solve the technical problem of low control accuracy of the robust controller in the permanent magnet synchronous motor control system. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the management method of the robust controller provided in the above-mentioned embodiment, and will not be repeated here.

[0136] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A management method for a robust controller, characterized in that: The management method of the robust controller is applied to a permanent magnet synchronous motor, and the method comprises: Obtaining the weighting function structure of the robust controller; Optimizing the parameters in the weighted function structure according to an improved particle swarm optimization algorithm; Acquire a weighting function according to the parameters and the weighting function structure; The robust controller is solved by the weighting function.

2. The management method of the robust controller according to claim 1, characterized in that: The step of obtaining the weighted function structure of the robust controller comprises: Obtaining a first function structure, a second function structure and unknown constants; The weighted function structure of the robust controller is composed according to the first function structure, the second function structure and the unknown constant.

3. The management method of the robust controller according to claim 2, characterized in that: Before the step of optimizing the parameters in the weighted function structure according to the improved particle swarm optimization algorithm, the method comprises: Obtaining a first parameter, a second parameter, and a third parameter in the first function structure; Obtaining the fourth parameter, the fifth parameter and the sixth parameter in the second function structure; The first parameter, the second parameter, the third parameter, the fourth parameter, the fifth parameter, the sixth parameter and the unknown constant are used as parameters in the weighting function structure.

4. The management method of the robust controller according to claim 3, characterized in that: The step of optimizing the parameters in the weighted function structure according to the improved particle swarm optimization algorithm comprises: Initialize and improve IPSO population; The optimal values ​​of the parameters in the weighted function structure are determined by performing optimization search through IPSO.

5. The management method of the robust controller according to claim 4, characterized in that: The step of obtaining the weighting function according to the parameters and the weighting function structure comprises: Substituting the optimal value into the first function structure, the second function structure and the unknown constant to obtain a first function, a second function and an optimal constant respectively; The weighting function is composed according to the first function, the second function and the optimal constant.

6. The management method of the robust controller according to claim 5, characterized in that: Before the step of solving the robust controller by using the weighted function, the method further comprises: Determining singular values ​​of the first function and singular values ​​of the second function; When the singular value of the first function and the singular value of the second function are both lower than a first preset value, performing the step of solving the robust controller by using the weighted function; When the singular value of the first function or the second function is not lower than the first preset value, return to the step of performing optimization through IPSO to determine the optimal values ​​of the parameters in the weighted function structure.

7. The management method of the robust controller according to claim 1, characterized in that: After the step of solving the robust controller by the weighted function, the method further comprises: Simulating the robust controller and obtaining a simulation result graph; Comparing the tracking performance of the curves in the simulation result graph with those in the traditional control simulation graph; When the tracking performance of the curve in the simulation result diagram is higher than the tracking performance of the curve in the traditional control simulation diagram, selecting the robust controller to be applied to the permanent magnet synchronous motor; When the tracking performance of the curve in the simulation result graph is not higher than the tracking performance of the curve in the traditional control simulation graph, return to the step of optimizing the parameters in the weighted function structure according to the improved particle swarm optimization algorithm.

8. A management device for a robust controller, characterized in that: The device comprises: A structure acquisition module, used to obtain the weighted function structure of the robust controller; A parameter optimization module, used for optimizing the parameters in the weighted function structure according to an improved particle swarm optimization algorithm; A function acquisition module, used for acquiring a weighting function according to the parameters and the weighting function structure; The target solving module is used to solve the robust controller through the weighting function.

9. A management device for a robust controller, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the management method of the robust controller according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the management method of the robust controller according to any one of claims 1 to 7 are implemented.

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