Layered optimization strategy-based motor controller design optimization method and related device

Abstract

The invention discloses a motor controller design optimization method based on a hierarchical optimization strategy and a related device, and relates to the technical field of motor controllers, and the method comprises the steps: carrying out the optimization of performance parameters according to a performance objective function of a motor controller and the constraint conditions of the performance parameters, and obtaining a first performance solution set of the performance parameters, determining a first cost solution set of cost parameters corresponding to the first performance solution set of the performance parameters; according to the performance objective function, the constraint condition of the performance parameter, the cost objective function, the constraint condition of the cost parameter, the first performance solution set and the first cost solution set, performing joint optimization on the performance parameter and the cost parameter of the motor controller to obtain a joint solution set of the motor controller; and finally, according to the performance objective function and the cost objective function, selecting an optimal group of solutions of the performance parameters and the cost parameters from the joint solution set. The design of the high-performance motor controller can be realized under the condition of considering the performance and the cost.

Description

Technical Field

[0001] The present application relates to the technical field of motor controllers, and in particular, to a design optimization method and related device for a motor controller based on a hierarchical optimization strategy. Background Art

[0002] A motor controller is a core component in a motor drive system, and its performance directly affects the efficiency, response speed, and reliability of the entire drive system. In modern industrial and transportation fields, the demand for motor controllers is increasing day by day, especially in applications such as electric vehicles, robots, and automation equipment. With the development of technology, the design of motor controllers not only needs to focus on performance but also needs to take cost control into account. The production cost and operating efficiency of motor controllers directly affect the market competitiveness of products.

[0003] Traditional single-objective optimization methods often cannot comprehensively solve these complex design requirements. Multi-objective optimization techniques have emerged, which can find the best balance among multiple objectives, meet the requirements of different application scenarios, and thus achieve a double improvement in economic and technical benefits. However, the current multi-objective optimization techniques cannot achieve the design of high-performance motor controllers while taking both performance and cost into account. Summary of the Invention

[0004] The purpose of the present application is to provide a design optimization method and related device for a motor controller based on a hierarchical optimization strategy, which can achieve the design of a high-performance motor controller while taking both performance and cost into account.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] In the first aspect, the present application provides a design optimization method for a motor controller based on a hierarchical optimization strategy, characterized in that the design optimization method for a motor controller based on a hierarchical optimization strategy includes:

[0007] Obtain the performance parameters of the motor controller and the cost parameters corresponding to the performance parameters.

[0008] Set the constraint conditions of the performance parameters and the constraint conditions of the cost parameters.

[0009] Construct a performance objective function according to the performance parameters of the motor controller, and construct a cost objective function according to the cost parameters of the motor controller.

[0010] Optimize the performance parameters according to the performance objective function and the constraint conditions of the performance parameters to obtain a first performance solution set of the performance parameters; the first performance solution set includes several groups of solutions of the performance parameters.

[0011] Determine the first cost solution set of the cost parameters corresponding to the first performance solution set of the performance parameters; the first cost solution set includes several groups of solutions of the cost parameters.

[0012] According to the performance objective function, the constraint conditions of the performance parameters, the cost objective function, the constraint conditions of the cost parameters, the first performance solution set and the first cost solution set, jointly optimize the performance parameters and cost parameters of the motor controller to obtain the joint solution set of the motor controller; the joint solution set includes a second performance solution set and a second cost solution set corresponding to the second performance solution set.

[0013] According to the performance objective function and the cost objective function, select the optimal set of solutions of the performance parameters and the solutions of the cost parameters from the joint solution set.

[0014] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the motor controller design optimization method based on a hierarchical optimization strategy described in the first aspect.

[0015] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the motor controller design optimization method based on a hierarchical optimization strategy described in the first aspect.

[0016] In a fourth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the motor controller design optimization method based on a hierarchical optimization strategy described in the first aspect.

[0017] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0018] The present application provides an optimization method for the design of a motor controller based on a hierarchical optimization strategy and related devices. The method includes: First, obtain the performance parameters of the motor controller and the cost parameters corresponding to the performance parameters, set the constraint conditions for the performance parameters and the constraint conditions for the cost parameters, construct a performance objective function based on the performance parameters of the motor controller, and construct a cost objective function based on the cost parameters of the motor controller; Then, optimize the performance parameters according to the performance objective function and the constraint conditions of the performance parameters to obtain the first performance solution set of the performance parameters, and determine the first cost solution set of the cost parameters corresponding to the first performance solution set of the performance parameters; Next, jointly optimize the performance parameters and cost parameters of the motor controller according to the performance objective function, the constraint conditions of the performance parameters, the cost objective function, the constraint conditions of the cost parameters, the first performance solution set and the first cost solution set to obtain the joint solution set of the motor controller; Finally, select the optimal set of solutions for the performance parameters and the cost parameters from the joint solution set according to the performance objective function and the cost objective function. Compared with the prior art method of directly performing multi-objective optimization on the performance and cost of the motor controller, which cannot achieve the design of a high-performance motor controller while taking into account both performance and cost, the present application adopts the above-mentioned hierarchical optimization method. First, perform the first layer of optimization on the performance parameters of the motor controller to obtain the optimized performance parameter solution set (and the corresponding cost parameter solution set). Then, based on the optimized performance parameter solution set and cost parameter solution set, perform the second layer of joint optimization on the performance parameters and cost parameters of the motor controller, so as to obtain a parameter solution set that takes into account both the performance and cost of the motor controller. Finally, select the optimal set of performance parameters and cost parameters from it to achieve the best balance between performance and cost. It can be seen that the above-mentioned hierarchical optimization strategy of the present application can achieve the design of a high-performance motor controller while taking into account both performance and cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is an application environment diagram of an optimization method for the design of a motor controller based on a hierarchical optimization strategy in an embodiment of the present application;

[0021] Figure 2 It is a flowchart of an optimization method for the design of a motor controller based on a hierarchical optimization strategy provided in an embodiment of the present application;

[0022] Figure 3Schematic flowchart of an optimization method for motor controller design based on a hierarchical optimization strategy provided by another embodiment of the present application;

[0023] Figure 4 Schematic structural diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0024] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without making creative efforts shall fall within the protection scope of the present application.

[0025] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0026] The optimization method for motor controller design based on a hierarchical optimization strategy provided by the embodiments of the present application (also referred to as the multi-objective optimization method for motor controller based on a hierarchical optimization strategy in this article) can be applied to, for example Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, placed on the cloud or other servers. The terminal 102 can send the performance parameters of the motor controller and the corresponding cost parameters to the server 104. After the server 104 receives the performance parameters of the motor controller and the corresponding cost parameters, the server 104 sets the constraint conditions of the performance parameters and the constraint conditions of the cost parameters; constructs a performance objective function according to the performance parameters of the motor controller, and constructs a cost objective function according to the cost parameters of the motor controller; optimizes the performance parameters according to the performance objective function and the constraint conditions of the performance parameters to obtain the first performance solution set of the performance parameters; determines the first cost solution set of the cost parameters corresponding to the first performance solution set of the performance parameters; jointly optimizes the performance parameters and cost parameters of the motor controller according to the performance objective function, the constraint conditions of the performance parameters, the cost objective function, the constraint conditions of the cost parameters, the first performance solution set and the first cost solution set to obtain the joint solution set of the motor controller; selects the optimal set of solutions of the performance parameters and the solutions of the cost parameters from the joint solution set according to the performance objective function and the cost objective function. The server 104 can feedback the selected optimal set of solutions of the performance parameters and the solutions of the cost parameters to the terminal 102. In addition, in some embodiments, the design optimization method of the motor controller based on the hierarchical optimization strategy can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly process the performance parameters of the motor controller and the corresponding cost parameters, or the server 104 can obtain the performance parameters of the motor controller and the corresponding cost parameters from the data storage system for processing.

[0027] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0028] In an exemplary embodiment, as Figure 2 shown, a design optimization method of a motor controller based on a hierarchical optimization strategy is provided. This method is executed by a computer device, and can be specifically executed alone by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of the present application, taking this method applied to Figure 1 the server 104 in

[0029] Step 201, obtain the performance parameters of the motor controller and the cost parameters corresponding to the performance parameters.

[0030] Step 202, set the constraint conditions of the performance parameters and the constraint conditions of the cost parameters.

[0031] Step 203, construct a performance objective function according to the performance parameters of the motor controller, and construct a cost objective function according to the cost parameters of the motor controller.

[0032] Step 204, optimize the performance parameters according to the performance objective function and the constraint conditions of the performance parameters to obtain a first performance solution set of the performance parameters; the first performance solution set includes several groups of solutions of the performance parameters.

[0033] Step 205, determine a first cost solution set of the cost parameters corresponding to the first performance solution set of the performance parameters; the first cost solution set includes several groups of solutions of the cost parameters.

[0034] Step 206, jointly optimize the performance parameters and cost parameters of the motor controller according to the performance objective function, the constraint conditions of the performance parameters, the cost objective function, the constraint conditions of the cost parameters, the first performance solution set and the first cost solution set to obtain a joint solution set of the motor controller; the joint solution set includes a second performance solution set and a second cost solution set corresponding to the second performance solution set.

[0035] Step 207, select an optimal group of solutions of the performance parameters and the solutions of the cost parameters from the joint solution set according to the performance objective function and the cost objective function.

[0036] In another exemplary embodiment, step 204 specifically includes:

[0037] Optimize the performance parameters according to the performance objective function and the constraint conditions of the performance parameters by using a multi-objective sparrow algorithm to obtain a first performance solution set of the performance parameters.

[0038] Further, optimizing the performance parameters according to the performance objective function and the constraint conditions of the performance parameters by using a multi-objective sparrow algorithm to obtain a first performance solution set of the performance parameters specifically includes the following steps 204.1 to 204.12:

[0039] Step 204.1, initialize the number of iterations.

[0040] Step 204.2, randomly generate a group of sparrow individuals according to the constraint conditions of the performance parameters, and determine the generated group of sparrow individuals as the current group of sparrow individuals; wherein, the position of each sparrow individual represents a solution to a group of performance parameters; the speed of each sparrow individual represents the change amount of a solution to a group of performance parameters.

[0041] Step 204.3, evaluate the performance fitness of each sparrow individual according to the current position of each sparrow individual in the current group of sparrow individuals and the performance objective function, and obtain the current performance fitness value of each sparrow individual; when the current iteration number is the initial iteration number, the current position of the sparrow individual is the initial position of the sparrow individual.

[0042] Step 204.5, update the individual best position of each sparrow individual according to the current performance fitness value and the historical optimal performance fitness value of each sparrow individual in the current group of sparrow individuals; the update process of the individual best position of each sparrow individual specifically includes: when the current performance fitness value of the sparrow individual is better than the historical optimal performance fitness value of the sparrow individual, update the individual best position of the sparrow individual to the current position of the sparrow individual, and update the historical optimal performance fitness value of the sparrow individual to the current performance fitness value of the sparrow individual.

[0043] Step 204.6, determine the first global best position according to the individual best positions of all sparrow individuals in the current group of sparrow individuals.

[0044] Step 204.7, update the current speed and current position of each sparrow individual according to the individual best position of each sparrow individual and the first global best position, obtain the updated speed and updated position, and determine the sparrow individual corresponding to the updated speed and updated position as the sparrow individual after parameter update.

[0045] Step 204.8, determine the current performance fitness value of each sparrow individual after parameter update according to the updated position of each sparrow individual after parameter update and the performance objective function.

[0046] Step 204.9, select the sparrow individual with the optimal current performance fitness value from all the sparrow individuals after parameter update, and store it in the Pareto front set.

[0047] Step 204.10, add 1 to the iteration number, and determine whether the first preset iteration number is reached to obtain the first judgment result.

[0048] Step 204.11, if the first judgment result is negative, select the sparrow individual with the optimal current performance fitness value from all sparrow individuals in the Pareto front set, form a new group of sparrow individuals with all the sparrow individuals after parameter update, and determine the new group of sparrow individuals as the current group of sparrow individuals, then return to the step "Update the individual best position of each sparrow individual according to the current performance fitness value and the historical optimal performance fitness value of each sparrow individual in the current group of sparrow individuals".

[0049] Step 204.12, if the first judgment result is positive, determine the positions of all sparrow individuals in the Pareto front set as the first performance solution set of the performance parameters.

[0050] As an optional implementation manner, the determination process of the first global best position includes:

[0051] Select the optimal current performance fitness value from the current performance fitness values corresponding to the individual best positions of all sparrow individuals in the current group of sparrow individuals, and determine the individual best position of the sparrow individual corresponding to the optimal current performance fitness value as the first global best position.

[0052] As an optional implementation manner, in step 204.7, "Update the current speed and current position of each sparrow individual according to the individual best position of each sparrow individual and the first global best position to obtain the updated speed and updated position", specifically includes the following steps 204.7.1 to step 204.7.2:

[0053] Step 204.7.1, update the current speed of each sparrow individual according to the individual best position of each sparrow individual and the first global best position by using the speed update formula to obtain the updated speed of each sparrow individual; the speed update formula is:

[0054] V i (t + 1) = wV i (t) + c1·r1·(P i -X i (t)) + c2·r2(G - X i (t));

[0055] Wherein, V i (t + 1) represents the updated speed of sparrow individual i; V i(t) represents the current speed of sparrow individual i; w represents the inertia weight, which is used to control the influence degree of the current speed of the sparrow individual on the updated speed; c1 and c2 are both learning factors, representing the attention degree of the sparrow individual to its own individual best position and the global best position; r1 and r2 are both random numbers between [0, 1], which are used to introduce randomness; P i represents the individual best position of sparrow individual i; G represents the first global best position; X i (t) represents the current position of sparrow individual i at time t; t + 1 represents the next moment of time t.

[0056] Step 204.7.2, according to the updated speed and the current position of each sparrow individual, using the position update formula, update the current position of each sparrow individual to obtain the updated position of each sparrow individual; the position update formula is:

[0057] X i (t + 1) = X i (t) + V i (t + 1);

[0058] where, X i (t + 1) represents the updated position of sparrow individual i.

[0059] In another exemplary embodiment, step 206 specifically includes:

[0060] According to the performance objective function, the constraint conditions of the performance parameters, the cost objective function, the constraint conditions of the cost parameters, the first performance solution set and the first cost solution set, adopting a multi-objective sparrow algorithm, jointly optimize the performance parameters and cost parameters of the motor controller to obtain the joint solution set of the motor controller.

[0061] Further, according to the performance objective function, the constraint conditions of the performance parameters, the cost objective function, the constraint conditions of the cost parameters, the first performance solution set and the first cost solution set, adopting a multi-objective sparrow algorithm, jointly optimize the performance parameters and cost parameters of the motor controller to obtain the joint solution set of the motor controller, which specifically includes the following steps 206.1 to step 206.13:

[0062] Step 206.1, initialize the number of iterations.

[0063] Step 206.2, according to the first performance solution set and the first cost solution set, determine the initial position of each sparrow individual in the current group of sparrow individuals, and randomly initialize the speed of each sparrow individual in the current group of sparrow individuals according to the constraint conditions of the performance parameters and the constraint conditions of the cost parameters.

[0064] Step 206.3: According to the current positions of each sparrow individual in the current group of sparrow individuals and the performance objective function, evaluate the performance fitness of each sparrow individual to obtain the current performance fitness value of each sparrow individual; when the current iteration number is the initial iteration number, the current position of the sparrow individual is the initial position of the sparrow individual.

[0065] Step 206.4: According to the current positions of each sparrow individual in the current group of sparrow individuals and the cost objective function, evaluate the cost fitness of each sparrow individual to obtain the current cost fitness value of each sparrow individual.

[0066] Step 206.5: Determine the current comprehensive fitness value of each sparrow individual according to the current performance fitness value and the current cost fitness value of each sparrow individual in the current group of sparrow individuals.

[0067] Step 206.6: Update the individual best position of each sparrow individual according to the current comprehensive fitness value and the historical optimal comprehensive fitness value of each sparrow individual in the current group of sparrow individuals; the update process of the individual best position of each sparrow individual specifically includes: when the current comprehensive fitness value of the sparrow individual is better than the historical optimal comprehensive fitness value of the sparrow individual, update the individual best position of the sparrow individual to the current position of the sparrow individual, and update the historical optimal comprehensive fitness value of the sparrow individual to the current comprehensive fitness value of the sparrow individual.

[0068] Step 206.7: Determine the second global best position according to the individual best positions of all sparrow individuals in the current group of sparrow individuals.

[0069] Step 206.8: Update the current speed and the current position of each sparrow individual according to the individual best position of each sparrow individual and the second global best position to obtain the updated speed and the updated position, and determine the sparrow individual corresponding to the updated speed and the updated position as the sparrow individual after parameter update.

[0070] Step 206.9: Determine the current comprehensive fitness value of each sparrow individual after parameter update according to the updated position, the performance objective function and the cost objective function of each sparrow individual after parameter update.

[0071] Step 206.10: Select the sparrow individual with the optimal current comprehensive fitness value from all the sparrow individuals after parameter update and store it in the Pareto front set.

[0072] Step 206.11, increment the iteration count by 1, and determine whether the second preset iteration count is reached to obtain a first determination result.

[0073] Step 206.12, if the first determination result is negative, select the sparrow individual with the optimal current comprehensive fitness value from all sparrow individuals within the Pareto front set, form a new group of sparrow individuals by combining it with all the sparrow individuals after parameter update, and determine the new group of sparrow individuals as the current group of sparrow individuals, then return to the step "Update the individual best position of each sparrow individual according to the current comprehensive fitness value and the historical optimal comprehensive fitness value of each sparrow individual in the current group of sparrow individuals".

[0074] Step 206.13, if the first determination result is positive, determine the positions of all sparrow individuals within the Pareto front set as the joint solution set of the motor controller.

[0075] As an optional implementation manner, the determination process of the second global best position includes:

[0076] Select the optimal current comprehensive fitness value from the current comprehensive fitness values corresponding to the individual best positions of all sparrow individuals in the current group of sparrow individuals, and determine the individual best position of the sparrow individual corresponding to the optimal current comprehensive fitness value as the second global best position.

[0077] As Figure 3 shown, the multi-objective optimization method for a motor controller based on a hierarchical optimization strategy provided by this application mainly includes three major parts:

[0078] 1) Optimization objectives of the motor controller and corresponding models: clarify the optimization objectives and constraint conditions, including performance indicators of the motor controller (such as efficiency, response time, and control accuracy) and cost limitations.

[0079] 2) Hierarchical optimization strategy: adopt a hierarchical optimization strategy to decompose the optimization problem into multiple levels and handle different objectives separately.

[0080] 3) Multi-objective sparrow algorithm for optimization and solution: use the multi-objective sparrow algorithm for iterative search, dynamically adjust parameters to improve the optimization effect; evaluate the performance of the current solution in each iteration and make adaptive adjustments according to the feedback information. Finally, select the best design scheme through Pareto front analysis to achieve the efficient and low-cost design of the motor controller. The entire system can significantly improve the control accuracy, dynamic performance, and robustness of the motor through hierarchical problem decomposition and adaptive optimization.

[0081] In an exemplary embodiment, the design objectives of the motor controller mainly include the following aspects:

[0082] 1) Performance targets and corresponding models:

[0083] Efficiency: Based on the input power and output power of the motor, the efficiency (i.e., the model of the efficiency target) is defined as:

[0084]

[0085] where P out is the power output by the motor, and P in is the power input to the motor. The minimum efficiency requirements (i.e., constraints) of the motor controller can be set under different operating conditions.

[0086] Response time: Including the acceleration time and deceleration time of the motor, the dynamic characteristics of the motor system can be described using a transfer function, such as:

[0087]

[0088] where K is the system gain, T is the time constant, and s is the Laplace variable.

[0089] Control accuracy: Through error analysis, the control accuracy is defined as the error between the actual output and the target output:

[0090] Error = |y actual - y target |;

[0091] where y actual is the actual output, and y target is the desired output.

[0092] 2) Cost targets and corresponding models:

[0093] Material cost: The material budget of the motor controller can be calculated using the following formula:

[0094]

[0095] where C material represents the material cost, C i is the unit price of material i, and Q i is the quantity of material i used. Manufacturing cost: Set the cost limit of the manufacturing process, including labor costs, equipment depreciation, etc., which can be expressed by the following formula:

[0096] C manufacturing = C labor + C equipment + C overhead ;

[0097] where C manufacturing represents the manufacturing cost, C labor and Cequipment and C overhead respectively represent labor cost, equipment depreciation, and indirect expenses.

[0098] The total cost C total :

[0099] C total = C material + C manufacturing .

[0100] In this application for multi-objective optimization, a hierarchical strategy is adopted to divide the optimization problem into multiple levels, and different objectives are processed separately. For example, in the first level, the following performance objective function can be adopted:

[0101] min F(x) = -αη + βG(s) + γError;

[0102] where x is the performance decision variable; α, β, and γ are weight coefficients reflecting the importance of each objective.

[0103] The efficiency constraint is set as:

[0104] 0 ≤ η ≤ 1.

[0105] Of course, in other embodiments, the efficiency constraint can also be set between the minimum efficiency value and 1.

[0106] The response time constraint is set as:

[0107] 0 ≤ G(s) ≤ G(s) max ;

[0108] where G(s) max represents the maximum value of the response time, and G(s) represents the response time, such as the motor acceleration time or deceleration time, or the sum of the two.

[0109] The control accuracy constraint is set as:

[0110] 0 ≤ Error ≤ Error max ;

[0111] where Error max represents the maximum value of the error between the actual output and the target output.

[0112] The multi-objective sparrow algorithm (MOPSO) is used for preliminary optimization to obtain a set of Pareto optimal solutions, that is, to find the optimal performance solution set (the first performance solution set). The found optimal performance solution set can be expressed as follows:

[0113] F(x) = [η, G(s), Error].

[0114] In the second layer, based on the first layer, the following cost objective function can be adopted:

[0115] min C(u) = C total ;

[0116] where u is the cost decision variable.

[0117] The total cost constraint is set as:

[0118]

[0119] where represents the maximum value of the total cost. Joint optimization of performance parameters and cost parameters is carried out in the second layer.

[0120] The multi-objective sparrow algorithm is used for iterative search, and the design parameters are dynamically adjusted to improve the optimization effect. The multi-objective sparrow algorithm is an algorithm optimized by simulating the foraging behavior of sparrows, and its basic steps are briefly described as follows:

[0121] 1) Initialization: Randomly generate a group of sparrow individuals. A sparrow individual can be regarded as a candidate solution, representing a specific set of motor controller design parameters, such as the above-mentioned performance parameters or the combination of performance parameters and cost parameters. Initialize its position and speed. Each "sparrow" represents a set of design parameters. It is equivalent to randomly generating a set of design parameters within the set parameter range (i.e., meeting the constraints).

[0122] 2) Evaluate fitness: Evaluate the fitness of each individual according to the objective function of each optimization goal to ensure that it meets the requirements in terms of both cost and performance.

[0123] 3) Update position and speed: Update the position and speed of the individual according to the current best position and the influence of other sparrow individuals.

[0124] Update the individual's best position: If the current fitness value (such as the current performance fitness value) is higher than the individual's historical best fitness value, update the individual's best position.

[0125] Update the global best position: Find the sparrow individual with the highest fitness value among all sparrow individuals and update the global best position.

[0126] Update speed: Calculate and update the speed of each sparrow individual according to the speed update formula. The speed update formula can be found in the previous embodiment.

[0127] Update position: Calculate and update the position of each sparrow individual according to the position update formula (see the previous embodiment).

[0128] 4) Introduce the concept of Pareto front, select the design solution that achieves the best balance between cost and performance from the multi-objective optimization results, and ensure the diversity of the optimization solutions.

[0129] Initialize the Pareto front set: Create an empty list to store the Pareto optimal solutions.

[0130] Compare individuals: Traverse all the recorded sparrow individuals. For example, for each sparrow individual A, compare it with other sparrow individuals B, and check if there exists a sparrow individual B that can be better than A in both objectives. If not, then A is a Pareto optimal solution and is added to the Pareto front set. By selecting the sparrow individuals in the Pareto front and then performing the next round of speed and position updates, the algorithm can continue to explore near the high-quality solutions, thus helping to find more Pareto optimal solutions.

[0131] 5) Iteration: Repeat steps 2) and 3) until the preset number of iterations is reached.

[0132] The multi-objective optimization method for motor controllers based on a hierarchical optimization strategy provided by this application innovatively introduces a dynamic adaptive mechanism and a multi-level optimization framework, enabling more effective balancing of the conflicts between complex objectives when performing multi-objective optimization of performance and cost in the design of motor controllers. Through the synergistic effect of intelligent algorithms, this method significantly improves the efficiency and convergence speed of optimization, solves the common local optimum problem in traditional methods, thus realizing the design of high-performance and low-cost motor controllers, promoting the competitiveness of products in the market, and meeting the rapidly changing market demands and the goals of sustainable development.

[0133] This application can significantly improve the design efficiency and effect of motor controllers. The specific technical effects include: The optimized motor controller can operate at a higher efficiency, with improved response time and control accuracy; Through refined cost optimization, the overall manufacturing and material costs are significantly reduced, enhancing the market competitiveness; Achieve a good balance between performance and cost, meet diverse market demands, and adapt to different application scenarios.

[0134] In an exemplary embodiment, a computer device is provided. This computer device can be a server or a terminal, and its internal structure diagram can be as Figure 4As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used for the performance parameters of the motor controller and the cost parameters corresponding to the performance parameters. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements an optimization method for the design of a motor controller based on a hierarchical optimization strategy.

[0135] Those skilled in the art can understand that Figure 4 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0136] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0137] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0138] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0139] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memories (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0140] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0141] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0142] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application; at the same time, for those of ordinary skill in the art, according to the ideas of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. An optimization method for the design of a motor controller based on a hierarchical optimization strategy, characterized in that, The design optimization method of the motor controller based on the hierarchical optimization strategy includes: Obtain the performance parameters of the motor controller and the cost parameters corresponding to the performance parameters; Set the constraint conditions of the performance parameters and the constraint conditions of the cost parameters; Construct a performance objective function according to the performance parameters of the motor controller and a cost objective function according to the cost parameters of the motor controller; Optimize the performance parameters according to the performance objective function and the constraint conditions of the performance parameters to obtain the first performance solution set of the performance parameters; the first performance solution set includes several sets of solutions of the performance parameters; Determine the first cost solution set of the cost parameters corresponding to the first performance solution set of the performance parameters; the first cost solution set includes several sets of solutions of the cost parameters; Jointly optimize the performance parameters and cost parameters of the motor controller according to the performance objective function, the constraint conditions of the performance parameters, the cost objective function, the constraint conditions of the cost parameters, the first performance solution set and the first cost solution set to obtain the joint solution set of the motor controller; the joint solution set includes a second performance solution set and a second cost solution set corresponding to the second performance solution set; Select the optimal set of solutions of the performance parameters and the solutions of the cost parameters from the joint solution set according to the performance objective function and the cost objective function.

2. The optimization method for motor controller design based on the hierarchical optimization strategy according to claim 1, characterized in that, Optimizing the performance parameters according to the performance objective function and the constraint conditions of the performance parameters to obtain the first performance solution set of the performance parameters specifically includes: Optimizing the performance parameters according to the performance objective function and the constraint conditions of the performance parameters by using a multi-objective sparrow algorithm to obtain the first performance solution set of the performance parameters.

3. The optimization method for the design of a motor controller based on a hierarchical optimization strategy according to claim 2, characterized in that, Optimizing the performance parameters according to the performance objective function and the constraint conditions of the performance parameters by using a multi-objective sparrow algorithm to obtain the first performance solution set of the performance parameters specifically includes: Initialize the number of iterations; Randomly generate a group of sparrow individuals according to the constraint conditions of the performance parameters, and determine the generated group of sparrow individuals as the current group of sparrow individuals; wherein, the position of each sparrow individual represents a set of solutions of the performance parameters; the speed of each sparrow individual represents the change amount of a set of solutions of the performance parameters; Evaluate the performance fitness of each sparrow individual according to the current position of each sparrow individual in the current group of sparrow individuals and the performance objective function to obtain the current performance fitness value of each sparrow individual; when the current number of iterations is the initial number of iterations, the current position of the sparrow individual is the initial position of the sparrow individual; Update the individual best position of each sparrow individual in the current group of sparrow individuals according to the current performance fitness value and the historical optimal performance fitness value of each sparrow individual; the update process of the individual best position of each sparrow individual specifically includes: when the current performance fitness value of the sparrow individual is better than the historical optimal performance fitness value of the sparrow individual, update the individual best position of the sparrow individual to the current position of the sparrow individual, and update the historical optimal performance fitness value of the sparrow individual to the current performance fitness value of the sparrow individual; Determine the first global best position according to the individual best positions of all sparrow individuals in the current group of sparrow individuals; Update the current speed and current position of each sparrow individual according to the individual best position of each sparrow individual and the first global best position to obtain the updated speed and updated position, and determine the sparrow individual corresponding to the updated speed and updated position as the sparrow individual after parameter update; Determine the current performance fitness value of each sparrow individual after parameter update according to the updated position of each sparrow individual after parameter update and the performance objective function; Select the sparrow individual with the optimal current performance fitness value from all the sparrow individuals after parameter update and store it in the Pareto front set; Increment the iteration count by 1 and determine whether the first preset iteration count is reached to obtain the first judgment result; If the first judgment result is no, select the sparrow individual with the optimal current performance fitness value from all the sparrow individuals in the Pareto front set, form a new group of sparrow individuals with all the sparrow individuals after parameter update, and determine the new group of sparrow individuals as the current group of sparrow individuals, and return to the step "Update the individual best position of each sparrow individual according to the current performance fitness value and the historical optimal performance fitness value of each sparrow individual in the current group of sparrow individuals"; If the first judgment result is yes, determine the positions of all the sparrow individuals in the Pareto front set as the first performance solution set of the performance parameters.

4. The optimization method for motor controller design based on a hierarchical optimization strategy according to claim 3, wherein The determination process of the first global best position includes: Select the optimal current performance fitness value from the current performance fitness values corresponding to the individual best positions of all sparrow individuals in the current group of sparrow individuals, and determine the individual best position of the sparrow individual corresponding to the optimal current performance fitness value as the first global best position.

5. The optimization method for motor controller design based on the hierarchical optimization strategy according to claim 4, characterized in that The update of the current speed and current position of each sparrow individual according to the individual best position of each sparrow individual and the first global best position to obtain the updated speed and updated position specifically includes: Update the current speed of each sparrow individual according to the individual best position of each sparrow individual and the first global best position using the speed update formula to obtain the updated speed of each sparrow individual; the speed update formula is: V i (t + 1)= wV i (t)+ c1·r1·(P i -X i (t))+ c2·r2(G - X i (t)); Among them, V i (t + 1) represents the updated speed of sparrow individual i; V i (t) represents the current speed of sparrow individual i; w represents the inertia weight; c1 and c2 are both learning factors; r1 and r2 are both random numbers between [0, 1]; P i represents the individual best position of sparrow individual i; G represents the first global best position; X i (t) represents the current position of sparrow individual i at time t; t + 1 represents the next moment of time t; According to the updated speed and current position of each sparrow individual, the current position of each sparrow individual is updated by using the position update formula to obtain the updated position of each sparrow individual; the position update formula is: X i (t + 1)= X i (t)+ V i (t + 1); Among them, X i (t + 1) represents the updated position of sparrow individual i.

6. The optimization method for motor controller design based on the hierarchical optimization strategy according to claim 5, characterized in that According to the performance objective function, the constraint conditions of the performance parameters, the cost objective function, the constraint conditions of the cost parameters, the first performance solution set and the first cost solution set, the performance parameters and cost parameters of the motor controller are jointly optimized to obtain the joint solution set of the motor controller, specifically including: According to the performance objective function, the constraint conditions of the performance parameters, the cost objective function, the constraint conditions of the cost parameters, the first performance solution set and the first cost solution set, the multi-objective sparrow algorithm is used to jointly optimize the performance parameters and cost parameters of the motor controller to obtain the joint solution set of the motor controller.

7. The optimization method for the design of a motor controller based on a hierarchical optimization strategy according to claim 6, characterized in that, According to the performance objective function, the constraint conditions of the performance parameters, the cost objective function, the constraint conditions of the cost parameters, the first performance solution set and the first cost solution set, the multi-objective sparrow algorithm is used to jointly optimize the performance parameters and cost parameters of the motor controller to obtain the joint solution set of the motor controller, specifically including: Initialize the number of iterations; According to the first performance solution set and the first cost solution set, determine the initial position of each sparrow individual in the current group of sparrow individuals, and randomly initialize the speed of each sparrow individual in the current group of sparrow individuals according to the constraint conditions of the performance parameters and the constraint conditions of the cost parameters; According to the current position of each sparrow individual in the current group of sparrow individuals and the performance objective function, evaluate the performance fitness of each sparrow individual to obtain the current performance fitness value of each sparrow individual; when the current number of iterations is the initial number of iterations, the current position of the sparrow individual is the initial position of the sparrow individual; According to the current position of each sparrow individual in the current group of sparrow individuals and the cost objective function, evaluate the cost fitness of each sparrow individual to obtain the current cost fitness value of each sparrow individual; According to the current performance fitness value and the current cost fitness value of each sparrow individual in the current group of sparrow individuals, determine the current comprehensive fitness value of each sparrow individual; According to the current comprehensive fitness value and the historical optimal comprehensive fitness value of each sparrow individual in the current group of sparrow individuals, update the individual best position of each sparrow individual; the update process of the individual best position of each sparrow individual specifically includes: when the current comprehensive fitness value of the sparrow individual is better than the historical optimal comprehensive fitness value of the sparrow individual, update the individual best position of the sparrow individual to the current position of the sparrow individual, and update the historical optimal comprehensive fitness value of the sparrow individual to the current comprehensive fitness value of the sparrow individual; Determine the second global best position according to the individual best positions of all sparrow individuals in the current group of sparrow individuals; Update the current speed and current position of each sparrow individual according to the individual best position of each sparrow individual and the second global best position, to obtain the updated speed and updated position, and determine the sparrow individual corresponding to the updated speed and updated position as the sparrow individual after parameter update; Determine the current comprehensive fitness value of each sparrow individual after parameter update according to the updated position of each sparrow individual after parameter update, the performance objective function, and the cost objective function; Select the sparrow individual with the optimal current comprehensive fitness value from all the sparrow individuals after parameter update, and store it in the Pareto front set; Increment the iteration count by 1, and determine whether the second preset iteration count is reached to obtain a first determination result; If the first determination result is no, then select the sparrow individual with the optimal current comprehensive fitness value from all the sparrow individuals in the Pareto front set, form a new group of sparrow individuals with all the sparrow individuals after parameter update, and determine the new group of sparrow individuals as the current group of sparrow individuals, and return to the step "Update the individual best position of each sparrow individual according to the current comprehensive fitness value and the historical optimal comprehensive fitness value of each sparrow individual in the current group of sparrow individuals"; If the first determination result is yes, then determine the positions of all the sparrow individuals in the Pareto front set as the joint solution set of the motor controller.

8. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the motor controller design optimization method based on a hierarchical optimization strategy according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the motor controller design optimization method based on a hierarchical optimization strategy according to any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the motor controller design optimization method based on a hierarchical optimization strategy according to any one of claims 1-7.

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