Motor speed ring PI parameter self-tuning method, device and equipment and storage medium
By using the evaluation model to optimize the parameter scheme in the parameter scheme iteration of the motor speed ring PI parameters, the problem of traditional motor servo systems relying on experience and insufficient disturbance resistance in the setting of the speed ring PI parameters is solved, and the robustness and accuracy of the motor control system are improved.
Patent Information
- Application Number
- CN202411342767.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-25
- Publication Date
- 2025-05-16
AI Technical Summary
Traditional motor servo systems rely highly on the experience of technicians in the setting of speed ring PI parameters, and lack the ability to resist disturbances to different working conditions, resulting in problems such as untimely response and inaccurate control.
In the iteration of the parameter scheme of the motor speed ring PI parameters, the parameter scheme is evaluated and optimized using a preset evaluation model until the preset iteration termination conditions are met, and a parameter scheme that can take into account the response speed and transient response oscillation amplitude are found.
It achieves improved robustness and accuracy of the motor control system, and can maintain good response performance under different operating conditions.
Smart Images
Figure CN120016907A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of motor control technology, and in particular to a method, device, equipment and storage medium for self-tuning PI parameters of a motor speed loop. Background Art
[0002] The PMSM AC servo system has the characteristics of high efficiency, high position resolution, fast response, small torque fluctuation, and strong overload capacity. Therefore, it is widely used in the motor control of new energy vehicles; but high-performance servo systems not only require efficient motors, but also need to match efficient control strategies.
[0003] PID control has a simple structure, clear physical meaning of parameters and high control accuracy. It is the mainstream algorithm for current automotive motor control. Speed loop PI parameter tuning refers to the process of continuously adjusting the parameters of the controller so that the system can achieve the expected control target. It is essentially a process of continuous optimization. The traditional servo system parameter tuning is highly dependent on the experience of technicians and lacks the ability to resist disturbances under different working conditions. It will cause problems such as untimely response and inaccurate control. Therefore, speed loop PI parameter self-tuning has always been a hot topic for scholars and a technical difficulty that the industry urgently needs to solve. Summary of the invention
[0004] The present application provides a motor speed loop PI parameter self-tuning method, device, equipment and storage medium, which can solve the technical problems existing in the prior art.
[0005] In a first aspect, an embodiment of the present application provides a method for self-tuning a motor speed loop PI parameter, which adopts the following technical solution:
[0006] A method for self-tuning a motor speed loop PI parameter, the method comprising:
[0007] Determine a plurality of first-generation parameter schemes of the motor speed loop PI parameters within a basic setting range of the motor speed loop PI parameters;
[0008] According to a preset evaluation model, iterating a plurality of the parameter schemes in a direction of improving an evaluation result of the evaluation model until a preset iteration termination condition is met;
[0009] From the multiple parameter solutions that have completed iteration, determine the parameter solution with the highest evaluation result as the optimization solution; wherein,
[0010] The evaluation result of the evaluation model is obtained based on a first factor and a second factor. The first factor is related to the error between the actual speed and the target speed when the motor runs for a set time according to the parameter scheme, and the second factor is related to the error between the actual speed and the target speed and the set time when the motor runs for a set time according to the parameter scheme.
[0011] In combination with the first aspect, in one implementation, the first factor is the integral of the error between the actual speed and the target speed over the set time period when the motor runs for the set time period with the parameter scheme.
[0012] In combination with the first aspect, in one implementation, the second factor is the integral of the product of the error between the actual speed and the target speed and the set time period during the process of the motor running for the set time period with the parameter scheme.
[0013] In combination with the first aspect, in one implementation, according to the preset evaluation model, iterating multiple parameter schemes in a direction of improving the evaluation result of the evaluation model until a preset iteration termination condition is met, the iterative process of each parameter scheme includes the following steps:
[0014] According to the preset population expansion model and the parameter scheme, an iterative object set including multiple iterative objects is obtained;
[0015] updating each iteration object in the iteration set according to a preset update model, and determining multiple candidate results of the current round of iteration process according to whether the evaluation result of the updated iteration object is improved;
[0016] The candidate result with the highest evaluation result is determined as the iteration result of this round of iteration.
[0017] In combination with the first aspect, in one implementation, determining multiple candidate results of the current round of iteration process according to whether the evaluation result of the updated iteration object is improved includes the following steps:
[0018] If the first factor and the second factor of the updated iteration object do not decrease compared with the iteration object before the update, and at least one of them increases, it is considered that the evaluation result of the updated iteration object has increased and is determined as the candidate result of this round of iteration process;
[0019] If the first factor and the second factor of the updated iteration object are both lower than those of the iteration object before the update, the iteration object before the update is used as the candidate result of this round of iteration process;
[0020] If the first factor and the second factor of the updated iteration object are unchanged relative to the iteration object before updating, the first factors and the second factors of two other iteration objects adjacent to the iteration object before updating and the iteration object after updating are used to determine whether the evaluation result is improved.
[0021] In combination with the first aspect, in one implementation, if the first factor and the second factor of the updated iteration object are unchanged relative to the iteration object before the update, judging whether the evaluation result is improved by using the first factor and the second factor of two other iteration objects adjacent to the iteration object before the update and the iteration object after the update includes the following steps:
[0022] According to the first factor and the second factor of two other iterative objects whose values are adjacent to the iterative object before and after the update and the best first factor, the worst first factor, the best second factor and the worst second factor in the iterative object set, the congestion degree of the two iterative objects before and after the update is obtained;
[0023] If the congestion degree of the updated iteration object increases, it is confirmed that the evaluation result of the updated iteration object increases.
[0024] In combination with the first aspect, in one implementation, the congestion degrees of the two iterative objects before and after the update are obtained by taking the first factor and the second factor of two other adjacent iterative objects and the best first factor, the worst first factor, the best second factor, and the worst second factor in the iterative object set according to the iterative object before and after the update.
[0025] The crowding degree is the sum of the crowding degree of the first factor and the crowding degree of the second factor. The crowding degree of the first factor / the second factor is the ratio of the difference between the first factor / the second factor of two other adjacent iteration objects to the iteration object and the difference between the best first factor / the second factor and the worst first factor / the second factor.
[0026] In a second aspect, the embodiment of the present application provides a motor speed loop PI parameter self-tuning device, which adopts the following technical solution:
[0027] A motor speed loop PI parameter self-tuning device, the motor speed loop PI parameter self-tuning device comprising:
[0028] An initial scheme design module, which is configured to determine a plurality of initial generation parameter schemes of the motor speed loop PI parameters within a basic setting range of the motor speed loop PI parameters;
[0029] An iteration and selection module is configured to iterate multiple parameter schemes in a direction of improving the evaluation result of the evaluation model according to a preset evaluation model until a preset iteration termination condition is met; and, from the multiple parameter schemes that have completed the iteration, determine the parameter scheme with the highest evaluation result as the optimization scheme; wherein the evaluation result of the evaluation model is obtained according to a first factor and a second factor, the first factor is related to the error between the actual speed and the target speed during the motor runs for a set time with the parameter scheme, and the second factor is related to the error between the actual speed and the target speed and the set time during the motor runs for a set time with the parameter scheme.
[0030] In a third aspect, the embodiment of the present application provides a motor speed loop PI parameter self-tuning device, which adopts the following technical solution:
[0031] A motor speed loop PI parameter self-tuning device, the motor speed loop PI parameter self-tuning device comprising a processor, a memory, and a motor speed loop PI parameter self-tuning program stored in the memory and executable by the processor, wherein when the motor speed loop PI parameter self-tuning program is executed by the processor, the steps of the motor speed loop PI parameter self-tuning method as described above are implemented.
[0032] In a fourth aspect, an embodiment of the present application provides a storage medium, which adopts the following technical solution:
[0033] A storage medium stores a motor speed loop PI parameter self-tuning program, wherein when the motor speed loop PI parameter self-tuning program is executed by a processor, the steps of the motor speed loop PI parameter self-tuning method as described above are implemented.
[0034] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:
[0035] By intervening in the iteration process of the parameter scheme of the motor speed loop PI parameters by using the evaluation model to evaluate the parameter scheme, the iterative process can eventually show a trend of continuous improvement in the evaluation results. Since the evaluation results are obtained based on the first factor and the second factor, which can respectively reflect the response speed and transient response oscillation amplitude of the parameter scheme when controlling the motor operation, it is finally possible to find a parameter scheme that can take into account both the response speed and the transient response oscillation amplitude, thereby improving the robustness and accuracy of the motor control system. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] Figure 1 This is a flow chart of an embodiment of a method for self-tuning PI parameters of a motor speed loop of the present application;
[0037] Figure 2This is a functional module diagram of an embodiment of a motor speed loop PI parameter self-tuning device of the present application;
[0038] Figure 3 This is a schematic diagram of the hardware structure of the motor speed loop PI parameter self-tuning device involved in the embodiment of the present application. DETAILED DESCRIPTION
[0039] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0040] The PMSM AC servo system has the characteristics of high efficiency, high position resolution, fast response, small torque fluctuation, and strong overload capacity. Therefore, it is widely used in the motor control of new energy vehicles; but high-performance servo systems not only require efficient motors, but also need to match efficient control strategies.
[0041] PID control has a simple structure, clear physical meaning of parameters and high control accuracy. It is the mainstream algorithm for current automotive motor control. Speed loop PI parameter tuning refers to the process of continuously adjusting the parameters of the controller so that the system can achieve the expected control target. It is essentially a process of continuous optimization. The traditional servo system parameter tuning is highly dependent on the experience of technicians and lacks the ability to resist disturbances under different working conditions. It will cause problems such as untimely response and inaccurate control. Therefore, speed loop PI parameter self-tuning has always been a hot topic for scholars and a technical difficulty that the industry urgently needs to solve.
[0042] Based on the above problems, the present application provides a method, device, equipment and storage medium for self-tuning of the PI parameters of a motor speed loop. The key point of the invention is that, during the iteration process of the parameter scheme of the motor speed loop PI parameters, the evaluation model is used to coordinate the evaluation result of the parameter scheme to intervene in the iteration process, so that the iterative parameter scheme can finally show a trend of continuous improvement in the evaluation result. Since the evaluation result is obtained based on the first factor and the second factor, and the two can respectively reflect the response speed and transient response oscillation amplitude of the parameter scheme when controlling the operation of the motor, therefore, it is finally possible to find a parameter scheme that can take into account both the response speed and the transient response oscillation amplitude, thereby improving the robustness and accuracy of the control system.
[0043] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.
[0044] In a first aspect, an embodiment of the present application provides a method for self-tuning a motor speed loop PI parameter.
[0045] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the motor speed loop PI parameter self-tuning method of the present application. Figure 1 As shown in the figure, the motor speed loop PI parameter self-tuning method includes:
[0046] S100, determining a plurality of first-generation parameter schemes of the motor speed loop PI parameters within a basic setting range of the motor speed loop PI parameters;
[0047] Specifically, an initial parameter scheme needs to be given before iteration, and the initial parameter scheme in this embodiment is specifically selected using the basic setting range of the motor speed loop PI parameters. The selected parameter scheme can satisfy the control of the smooth operation of the motor. Each initial scheme will continue to improve in the effect of controlling the motor operation process according to subsequent iterative steps.
[0048] S200, according to a preset evaluation model, iterating a plurality of the parameter schemes in a direction of improving an evaluation result of the evaluation model until a preset iteration termination condition is met;
[0049] Specifically, the evaluation model evaluates the candidate solutions generated in the iteration process, and then selects the candidate results with higher evaluation results as the results of this round of iteration, and participates in the next round of iteration process. Finally, after multiple rounds of iterations, after the iteration termination conditions are met, the final parameter solution is obtained. The iteration termination conditions may be different in different embodiments, and may be a preset number of iterations, or whether the set evaluation result requirements are met, etc., which will not be elaborated here. As for the specific iteration method, different iteration models may be adopted for iteration in different embodiments, and this application does not limit it here.
[0050] S300, determining, from among the multiple parameter solutions that have completed iteration, the parameter solution with the highest evaluation result as the optimization solution;
[0051] Among them, the evaluation result of the evaluation model is obtained based on the first factor and the second factor, the first factor is related to the error between the actual speed and the target speed during the motor runs for a set time with the parameter scheme, and the second factor is related to the error between the actual speed and the target speed and the set time during the motor runs for a set time with the parameter scheme.
[0052] Specifically, the first factor is the integral of the error between the actual speed and the target speed over the set time period during the motor runs with the parameter scheme for the set time period; the second factor is the integral of the product of the error between the actual speed and the target speed and the set time period during the motor runs with the parameter scheme for the set time period. The calculation formulas of the first factor (LAE) and the second factor (LATE) are as follows:
[0053]
[0054]
[0055] Where V req is the target speed, V fed is the actual speed.
[0056] In this embodiment, during the iteration process of the parameter scheme of the motor speed loop PI parameters, the evaluation model is used to coordinate the evaluation results of the parameter scheme to intervene in the iteration process, so that the iterative parameter scheme can eventually show a trend of continuous improvement in the evaluation results. Since the evaluation results are obtained based on the first factor and the second factor, and the two can respectively reflect the response speed and transient response oscillation amplitude of the parameter scheme when controlling the operation of the motor, it is ultimately possible to find a parameter scheme that can take into account both the response speed and the transient response oscillation amplitude, thereby improving the robustness and accuracy of the control system.
[0057] Further, in one embodiment, in the step S200, according to a preset evaluation model, iterating a plurality of the parameter schemes in a direction of improving the evaluation result of the evaluation model until a preset iteration termination condition is satisfied, the iteration process of each parameter scheme comprises the following steps:
[0058] S210, obtaining an iteration object set including a plurality of iteration objects according to a preset population expansion model and the parameter scheme;
[0059] S220, updating each iteration object in the iteration set according to a preset update model, and determining multiple candidate results of the current round of iteration process according to whether the evaluation result of the updated iteration object is improved;
[0060] S230: Determine the candidate result with the highest evaluation result as the iteration result of this round of iteration.
[0061] In this embodiment, in step S210, in the process of obtaining an iterative object set according to a parameter scheme using a population expansion model, a crossover transformation and a shift transformation are specifically used to adjust the parameter scheme, and finally a plurality of iterative objects are obtained to form an iterative object set.
[0062] In step S220, the preset update model specifically uses the JAYA algorithm iteration formula to update multiple iteration objects in each iteration object set. The specific iteration formula of the JAYA algorithm is as follows:
[0063] X i,j,k +r b,j,k (X best,j,k -|X i,j,k |)-r w,j,k (X worst,j,k -|X i,j,k |)
[0064] X i,j,k , X' i,j,k They represent the values of the i-th individual of the k-th generation before and after iterative updating in the j-th dimension by Jaya formula, where i∈{1,2…,n} represents the i-th individual in the population, j∈{1,2,…,m} represents the j-th dimension variable of the individual, k represents the number of generations of the current iteration, and r b,j,k 、r w,j,k They represent the coefficients of the j-th dimension variable when it is updated in the kth generation, and are all random numbers between [0,1]. best,j,k , X worst,j,k Respectively represent the target values of the best and worst individuals determined by the above evaluation model after the kth generation update. b,j,k (X best,j,k -|X i,j,k |),-r w,j,k (X worst,j,k -|X i,j,k |) represent the direction in which the current individual moves towards the best solution and away from the worst solution, respectively.
[0065] Further, after obtaining the updated parameter solution, the evaluation model is used in step S220 to determine whether the updated parameter solution is a candidate result of this round of iteration, which specifically includes the following steps:
[0066] S220: If the first factor and the second factor of the updated iteration object are not decreased compared with the iteration object before the update, and at least one of them is improved, it is considered that the evaluation result of the updated iteration object is improved and is determined as the candidate result of this round of iteration process;
[0067] S221: If the first factor and the second factor of the updated iteration object are both lower than those of the iteration object before the update, the iteration object before the update is used as the candidate result of this round of iteration process;
[0068] Specifically, steps S220 and S221 can be understood by the following formula:
[0069] minf(X1)=min{f1(X1,f2(X1)},X1∈M,where f n (X1) represents the nth objective function. When it is 1, it represents the first factor mentioned above, and when it is 2, it represents the second factor. X1 represents the first iteration object in the iteration object set, and M is the iteration object set. If there is another solution X2∈M, for All meet and Satisfy f i (X1) <f i (X2), that is, X1 dominates X2, and solution X1 has a higher rank, that is, its evaluation result is better.
[0070] If the above method determines that the two parameter schemes before and after the update have the same level, further comparison is performed through step S222 provided by the present application:
[0071] S222: If the first factor and the second factor of the updated iteration object are unchanged relative to the iteration object before updating, determine whether the evaluation result is improved based on the first factors and second factors of two other iteration objects adjacent to the iteration object before updating and the iteration object after updating.
[0072] The determination process of step S222 includes the following steps:
[0073] S2221, obtaining the congestion degree of the two iterative objects before and after the update according to the first factor and the second factor of two other iterative objects whose values are adjacent to the iterative object before the update and the iterative object after the update, and the best first factor, the worst first factor, the best second factor, and the worst second factor in the iterative object set;
[0074] Specifically, the congestion degree in this embodiment is the sum of the congestion degree of the first factor and the congestion degree of the second factor. The congestion degree of the first factor / the second factor is the ratio of the difference between the first factor / the second factor of two other adjacent iteration objects to the iteration object to the difference between the best first factor / the second factor and the worst first factor / the second factor, which can be seen from the following formula:
[0075] Crowding
[0076] Wherein, n is the number of objective functions. In this embodiment, since the first factor and the second factor are included, n is 2; f i max With f i min are the maximum and minimum values of the i-th objective function in the population, respectively; s is the parameter scheme currently calculated, and f i (s+1) and f i(s-1) is the i-th objective function value of the two adjacent parameter schemes before and after the currently calculated parameter scheme.
[0077] S2222: If the congestion degree of the updated iteration object is improved, confirm that the evaluation result of the updated iteration object is improved.
[0078] In a second aspect, an embodiment of the present application also provides a motor speed loop PI parameter self-tuning device.
[0079] In one embodiment, referring to Figure 2 , Figure 2 This is a functional module diagram of an embodiment of the motor speed loop PI parameter self-tuning device of the present application. Figure 2 As shown, the motor speed loop PI parameter self-tuning device includes:
[0080] An initial scheme design module, which is configured to determine a plurality of initial generation parameter schemes of the motor speed loop PI parameters within a basic setting range of the motor speed loop PI parameters;
[0081] An iteration and selection module is configured to iterate multiple parameter schemes in a direction of improving the evaluation result of the evaluation model according to a preset evaluation model until a preset iteration termination condition is met; and, from the multiple parameter schemes that have completed the iteration, determine the parameter scheme with the highest evaluation result as the optimization scheme; wherein the evaluation result of the evaluation model is obtained according to a first factor and a second factor, the first factor is related to the error between the actual speed and the target speed during the motor runs for a set time with the parameter scheme, and the second factor is related to the error between the actual speed and the target speed and the set time during the motor runs for a set time with the parameter scheme.
[0082] Among them, the functional implementation of each module in the above-mentioned motor speed loop PI parameter self-tuning device corresponds to the steps in the above-mentioned motor speed loop PI parameter self-tuning method embodiment, and its functions and implementation processes are no longer repeated here one by one.
[0083] In a third aspect, an embodiment of the present application provides a motor speed loop PI parameter self-tuning device, which can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.
[0084] Reference Figure 3 , Figure 3 The hardware structure diagram of the motor speed loop PI parameter self-tuning device involved in the embodiment of the present application is shown in FIG. In the embodiment of the present application, the motor speed loop PI parameter self-tuning device may include a processor, a memory, a communication interface, and a communication bus.
[0085] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.
[0086] The communication interface includes an input / output (I / O) interface, a physical interface, and a logical interface, etc., which are used to interconnect the devices inside the motor speed loop PI parameter self-tuning device, and an interface used to interconnect the motor speed loop PI parameter self-tuning device with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.
[0087] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.
[0088] The processor may be a general-purpose processor, and the general-purpose processor may call the motor speed loop PI parameter self-tuning program stored in the memory, and execute the motor speed loop PI parameter self-tuning method provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). Among them, the method executed when the motor speed loop PI parameter self-tuning program is called may refer to the various embodiments of the motor speed loop PI parameter self-tuning method of the present application, which will not be repeated here.
[0089] Those skilled in the art will understand that Figure 3 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.
[0090] In a fourth aspect, an embodiment of the present application also provides a storage medium.
[0091] The storage medium of the present application stores a motor speed loop PI parameter self-tuning program, wherein when the motor speed loop PI parameter self-tuning program is executed by a processor, the steps of the motor speed loop PI parameter self-tuning method as described above are implemented.
[0092] Among them, the method implemented when the motor speed loop PI parameter self-tuning program is executed can refer to the various embodiments of the motor speed loop PI parameter self-tuning method of the present application, and will not be repeated here.
[0093] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.
[0094] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.
[0095] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.
[0096] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.
[0097] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.
[0098] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.
[0099] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.
Claims
1. A method for self-tuning motor speed loop PI parameters, characterized in that: The method comprises: Determine a plurality of first-generation parameter schemes of the motor speed loop PI parameters within a basic setting range of the motor speed loop PI parameters; According to a preset evaluation model, iterating a plurality of the parameter schemes in a direction of improving an evaluation result of the evaluation model until a preset iteration termination condition is met; From the multiple parameter solutions that have completed iteration, determine the parameter solution with the highest evaluation result as the optimization solution; Among them, the evaluation result of the evaluation model is obtained based on the first factor and the second factor, the first factor is related to the error between the actual speed and the target speed during the motor runs for a set time with the parameter scheme, and the second factor is related to the error between the actual speed and the target speed and the set time during the motor runs for a set time with the parameter scheme.
2. The motor speed loop PI parameter self-tuning method according to claim 1, characterized in that: The first factor is the integral of the error between the actual speed and the target speed over the set time period when the motor runs for the set time period with the parameter scheme.
3. The motor speed loop PI parameter self-tuning method according to claim 1, characterized in that: The second factor is the integral of the product of the error between the actual speed and the target speed and the set time period during the motor running for the set time period with the parameter scheme.
4. The motor speed loop PI parameter self-tuning method according to claim 1, characterized in that: In the step of iterating the plurality of parameter schemes in a direction of improving the evaluation result of the evaluation model according to the preset evaluation model until a preset iteration termination condition is satisfied, the iteration process of each parameter scheme comprises the following steps: According to the preset population expansion model and the parameter scheme, an iterative object set including multiple iterative objects is obtained; updating each iteration object in the iteration set according to a preset update model, and determining a plurality of candidate results of the current round of iteration process according to whether the evaluation result of the updated iteration object is improved; The candidate result with the highest evaluation result is determined as the iteration result of this round of iteration.
5. The motor speed loop PI parameter self-tuning method according to claim 4, characterized in that: The step of determining, according to whether the evaluation result of the updated iteration object is improved, a plurality of candidate results of the current iteration process comprises the following steps: If the first factor and the second factor of the updated iteration object do not decrease compared with the iteration object before the update, and at least one of them increases, it is considered that the evaluation result of the updated iteration object has increased and is determined as the candidate result of this round of iteration process; If the first factor and the second factor of the updated iteration object are both lower than those of the iteration object before the update, the iteration object before the update is used as the candidate result of this round of iteration process; If the first factor and the second factor of the updated iteration object are unchanged relative to the iteration object before updating, the first factors and the second factors of two other iteration objects adjacent to the iteration object before updating and the iteration object after updating are used to determine whether the evaluation result is improved.
6. The motor speed loop PI parameter self-tuning method according to claim 5, characterized in that: If the first factor and the second factor of the updated iteration object are unchanged relative to the iteration object before the update, judging whether the evaluation result is improved by using the first factors and the second factors of two other iteration objects adjacent to the iteration object before the update and the iteration object after the update, comprises the following steps: According to the first factor and the second factor of two other iterative objects whose values are adjacent to the iterative object before and after the update and the best first factor, the worst first factor, the best second factor and the worst second factor in the iterative object set, the congestion degree of the two iterative objects before and after the update is obtained; If the congestion degree of the updated iteration object increases, it is confirmed that the evaluation result of the updated iteration object increases.
7. The motor speed loop PI parameter self-tuning method according to claim 6, characterized in that: The congestion degree of the two iterative objects before and after the update is obtained by taking the first factor and the second factor of two other adjacent iterative objects and the best first factor, the worst first factor, the best second factor and the worst second factor in the iterative object set according to the iterative object before and after the update. The crowding degree is the sum of the crowding degree of the first factor and the crowding degree of the second factor. The crowding degree of the first factor / the second factor is the ratio of the difference between the first factor / the second factor of two other adjacent iteration objects to the iteration object and the difference between the best first factor / the second factor and the worst first factor / the second factor.
8. A motor speed loop PI parameter self-tuning device, characterized in that: The motor speed loop PI parameter self-tuning device comprises: An initial scheme design module, which is configured to determine a plurality of initial generation parameter schemes of motor speed loop PI parameters within a basic setting range of motor speed loop PI parameters; An iteration and selection module is configured to iterate multiple parameter schemes in a direction of improving the evaluation result of the evaluation model according to a preset evaluation model until a preset iteration termination condition is met; and, from the multiple parameter schemes that have completed the iteration, determine the parameter scheme with the highest evaluation result as the optimization scheme; wherein the evaluation result of the evaluation model is obtained according to a first factor and a second factor, the first factor is related to the error between the actual speed and the target speed during the motor runs for a set time with the parameter scheme, and the second factor is related to the error between the actual speed and the target speed and the set time during the motor runs for a set time with the parameter scheme.
9. A motor speed loop PI parameter self-tuning device, characterized in that: The motor speed loop PI parameter self-tuning device includes a processor, a memory, and a motor speed loop PI parameter self-tuning program stored in the memory and executable by the processor, wherein when the motor speed loop PI parameter self-tuning program is executed by the processor, the steps of the motor speed loop PI parameter self-tuning method as described in any one of claims 1 to 7 are implemented.
10. A storage medium, characterized in that: The storage medium stores a motor speed loop PI parameter self-tuning program, wherein when the motor speed loop PI parameter self-tuning program is executed by the processor, the steps of the motor speed loop PI parameter self-tuning method according to any one of claims 1 to 7 are implemented.