BLDCM control method based on intelligent fractional order controller

Abstract

The invention discloses a BLDCM control method based on an intelligent fractional order controller, and the method comprises the steps: building a transfer function of the intelligent fractional order controller, and enabling the transfer function to be used for controlling a BLDCM; parameters in a transfer function of the intelligent fractional order controller are obtained, and a training set is established; determining a parameter set in a transfer function of the intelligent fractional order controller based on the training set; determining a transfer function of the intelligent fractional order controller based on the parameter set; and controlling the BLDCM based on the transfer function and the control parameter of the intelligent fractional order controller. The problems that in current BLDCM control, a common PID control method and other methods are poor in self-adaptability, insufficient in control precision, low in control efficiency and the like are solved. Based on parameter screening in the transfer function of the intelligent fractional order controller, it is guaranteed that the intelligent adaptive controller has higher control precision and better adaptivity, and the control efficiency can be improved under the condition that the requirements are met.

Description

Technical Field

[0001] The invention belongs to the field of automatic control, and specifically relates to a BLDCM control method based on an intelligent fractional-order controller. Background Art

[0002] Brushless DC motor (BLDCM) has achieved good application results in many fields due to its good adaptability to various control methods. However, conventional control methods are better for scenarios where there are no excessive control requirements for control errors. For high-precision application scenarios, the operation accuracy of BLDCM is insufficient due to the limitations of the control system itself. In addition, although the coordinated application of intelligent fractional-order controller (FOPID) and BLDCM has been included in the research scope, the controllability, flexibility and accuracy of the system cannot meet the requirements of high-precision usage scenarios.

[0003] Therefore, how to achieve high-precision control of BLDCM based on FOPID is a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0004] The purpose of the present invention is to provide a BLDCM control method based on an intelligent fractional-order controller, thereby achieving high-precision control of the BLDCM. Specifically, it includes:

[0005] A BLDCM control method based on an intelligent fractional-order controller, the control method comprising:

[0006] Establishing a transfer function of an intelligent fractional-order controller for controlling the BLDCM;

[0007] Obtaining parameters in the transfer function of the intelligent fractional-order controller and establishing a training set;

[0008] Based on the training set, determining a parameter set in a transfer function of the intelligent fractional-order controller;

[0009] Based on the parameter set, determining a transfer function of the intelligent fractional-order controller;

[0010] The BLDCM is controlled based on the intelligent fractional-order controller transfer function and control parameters.

[0011] Optionally, establishing an intelligent fractional-order controller transfer function for controlling the BLDCM includes:

[0012] The transfer function of the intelligent fractional-order controller is obtained. The transfer function of the intelligent fractional-order controller is:

[0013] Gc (s) = K p +K i s -λ +K d s μ

[0014] Among them, G c (s) represents the output of the transfer function; K p Represents proportional gain; K i Indicates the integral gain; K d represents the differential gain; λ represents the order of the integral term; μ represents the order of the differential term; s represents the complex frequency domain variable in the Laplace transform;

[0015] The BLDCM is controlled based on the transfer function output of the intelligent fractional-order controller.

[0016] Optionally, the obtaining parameters in the transfer function of the intelligent fractional-order controller and establishing a training set includes:

[0017] Setting parameters in the transfer function of the intelligent fractional-order controller to obtain setting parameters;

[0018] The setting parameters are set to different training sets according to parameter categories to obtain different parameter training sets.

[0019] Optionally, the setting parameters are set into different training sets according to parameter categories to obtain different parameter training sets, including:

[0020] The parameter categories are classified according to the proportional gain, integral gain, differential gain, integral term order, and differential term order;

[0021] Initially setting the classified parameters to obtain initial setting parameters, wherein the initial setting is determined according to the maximum value and the minimum value of the parameter category;

[0022] Setting an increase or decrease step length for the initial parameters, performing increase or decrease processing on the initial parameters according to the increase or decrease step length, and obtaining a preset parameter sequence;

[0023] The preset parameter sequence is set to the corresponding classification data set to obtain a parameter training set.

[0024] Optionally, determining a parameter set in a transfer function of the intelligent fractional-order controller based on the parameter training set includes:

[0025] Performing algorithm optimization on the initial parameters in the training set includes:

[0026] Establish an adaptive optimization factor, the adaptive optimization factor equation is:

[0027]

[0028] Among them, α1 represents the maximum value of the adaptive optimization factor, α0 represents the minimum value of the adaptive optimization factor, and α i represents the adaptive optimization factor, F i represents the fitness of the particle, F gbest Represents the global minimum fitness, ABS() represents the absolute value, and MIN() represents the minimum value;

[0029] Based on the adaptive optimization factor, the particle position is determined, and the equation is:

[0030]

[0031] Among them, x i (t+1) represents the position of the particle at time (t+1), p i (t) represents the local attractor position of the particle at time t, α i represents the adaptive optimization factor, C(t) represents the center point position, x i (t) represents the position of the particle at time t, g represents a random number with Gaussian probability distribution, and k represents the value interval number;

[0032] Based on the position result of the particle at time (t+1), updating the optimal position of the particle;

[0033] Obtaining the particle parameters corresponding to the optimal position of the particle;

[0034] Based on the parameter category, the particle parameters are set into a parameter set established based on the parameter category.

[0035] Optionally, determining the intelligent fractional-order controller transfer function based on the parameter set includes:

[0036] Acquire all the parameter combinations in different parameter sets to obtain a data group;

[0037] The parameters in the data group are set to the transfer function of the intelligent fractional-order controller to obtain the transfer function of the intelligent fractional-order controller.

[0038] Optionally, also include:

[0039] Inputting a control signal to the intelligent fractional-order controller and obtaining actual operating parameters of the BLDCM corresponding to the control signal;

[0040] Based on the control signal, obtaining the expected operating parameters of the BLDCM corresponding to the control signal;

[0041] An error rate between the actual operating parameter of the BLDCM and the expected operating parameter of the BLDCM is calculated, and if the error rate is within an error rate threshold, the parameters in the parameter set are set in the intelligent fractional order controller transfer function.

[0042] Optionally, controlling the BLDCM based on the intelligent fractional-order controller transfer function and control parameters includes:

[0043] Based on the parameter set, establishing the intelligent fractional-order controller transfer function;

[0044] Sending control parameters to the intelligent fractional-order controller to obtain a control signal;

[0045] Based on the control signal, the BLDCM is controlled.

[0046] The beneficial effects of the present invention are as follows:

[0047] 1. High-quality screening of intelligent fractional-order controller parameters is achieved. For an intelligent fractional-order controller to be able to play a good control role, it is obviously necessary to control based on a control transfer function. In this application, based on relevant algorithms, parameter sets of multiple transfer functions are obtained and the parameter sets are verified to obtain more accurate parameter results.

[0048] 2. Improve control accuracy. The operating accuracy of BLDCM depends on the controller. The control is based on the intelligent fractional-order controller. Since the controller introduces integral order and differential order, the system can have better control accuracy and flexibility. The intelligent algorithm is combined with the intelligent fractional-order controller to further improve the adaptive ability of the intelligent fractional-order controller, thereby improving the control accuracy of BLDCM.

[0049] 3. Possessing adaptive capability. The intelligent fractional-order controller and the intelligent algorithm in this application are processed in a coordinated manner. In this way, the relevant parameters in the intelligent fractional-order controller can be adaptively adjusted, and multiple parameter sets can be generated, so that the parameters can be adaptively adjusted based on the control requirements, so that the intelligent fractional-order controller has adaptive capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. The drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific implementation methods, they are used to explain the present disclosure, but do not constitute a limitation to the present disclosure. In the drawings:

[0051] Figure 1 A flow chart of a BLDCM control method based on an intelligent fractional-order controller provided in an embodiment of the present application;

[0052] Figure 2 A diagram showing the BLDCM speed control results based on the PID algorithm and the ASP-PID algorithm provided in the embodiment of the present application;

[0053] Figure 3 A fitness value convergence curve diagram of a BLDCM control method based on an intelligent fractional-order controller provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In addition, in the embodiments of the present invention, "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0055] At present, BLDCM has achieved good application results in many fields, such as electric vehicle motors, robot joints, etc. No matter in which application field, the control accuracy of BLDCM and the adaptability of the control process need to be considered. Only in this way can we avoid the difference between the operation results of BLDCM and the actual requirements. For the control of BLDCM, feedback control is an effective control method. For this reason, the PID control method has been widely used to make it run. However, in specific applications, the PID control method often has problems such as the control cannot be too large, the control adaptability is insufficient, and the control time is too long. The existence of such problems obviously cannot fully and effectively play the effect of BLDCM.

[0056] The present application provides a BLDCM control method based on an intelligent fractional-order controller, which improves the adaptability and accuracy of the BLDCM control process.

[0057] like Figure 1 As shown, it is a flow chart of a BLDCM control method based on an intelligent fractional-order controller provided in an embodiment of the present application, specifically:

[0058] S110 , establishing a transfer function of an intelligent fractional-order controller for controlling the BLDCM.

[0059] S120: Obtain parameters in the transfer function of the intelligent fractional-order controller and establish a training set.

[0060] S130. Determine a parameter set in a transfer function of the intelligent fractional-order controller based on the training set.

[0061] S140. Determine the intelligent fractional-order controller transfer function based on the parameter set.

[0062] S150: Control the BLDCM based on the intelligent fractional-order controller transfer function and control parameters.

[0063] The purpose of the above steps is to perform intelligent processing on the intelligent fractional-order controller, so as to screen and process all parameters in the current intelligent fractional-order controller transfer function based on the intelligent algorithm, and then select the transfer function parameters in the intelligent fractional-order controller for the obtained parameters to realize the control of the BLDCM.

[0064] For the above different steps, other technical support needs to be established, specifically:

[0065] Step S110 includes:

[0066] The transfer function of the intelligent fractional-order controller is obtained. The transfer function of the intelligent fractional-order controller is:

[0067] G c (s) = K p +K i s -λ +K d s μ

[0068] Among them, G c (s) represents the output of the transfer function; K p Represents proportional gain; K i Indicates the integral gain; K drepresents the differential gain; λ represents the order of the integral term; μ represents the order of the differential term; s represents the complex frequency domain variable in the Laplace transform.

[0069] For intelligent fractional-order controllers, the transfer function is actually an existing technology, but the relevant parameters in the transfer function, especially the order of the integral term and the order of the differential term, need to be confirmed with high precision. Only in this case can the problem of low control accuracy in the specific application of the intelligent fractional-order controller be avoided.

[0070] The BLDCM is controlled based on the transfer function output of the intelligent fractional-order controller.

[0071] Among them, the current intelligent fractional-order controller is processed with data, and all parameters in the transfer function of the intelligent fractional-order controller are calculated and determined based on the intelligent algorithm. After the control result is obtained, the specific transfer function of the intelligent fractional-order controller is obtained.

[0072] The obtained transfer function of the intelligent fractional-order controller needs to receive a control signal for the BLDCM, so as to send a control signal to the BLDCM based on the output of the transfer function.

[0073] Among them, after BLDCM obtains the output of the intelligent fractional-order controller, BLDCM can operate according to the output signal.

[0074] After obtaining the transfer function of the intelligent fractional-order controller, it is necessary to determine the parameters in the transfer function, specifically:

[0075] As described in step S120, it is necessary to confirm the various parameters in the transfer function so as to use the transfer function to control the BLDCM. Specifically:

[0076] Setting parameters in the transfer function of the intelligent fractional-order controller to obtain setting parameters;

[0077] The setting parameters are set to different training sets according to parameter categories to obtain different parameter training sets.

[0078] The purpose of this step is to further screen and verify the data obtained from the already set parameters based on the initial settings of the parameters, and to obtain the most suitable parameters.

[0079] Among them, for all parameters in the transfer function, maximum and minimum values ​​are set for them, and all set parameters therein are set to the corresponding parameter training set.

[0080] Among them, all parameter training sets need to be confirmed completely based on the current parameter type and parameter performance to ensure that the data in the current parameter training set are of the same type in the obtained parameters.

[0081] Among them, for the parameter processing in the parameter training set, it is also necessary to set the parameters in all the parameter training sets according to the corresponding data value step.

[0082] Among them, for all the obtained parameter training sets, it is necessary to identify the parameters in the parameter training set and label the parameter types, so that the obtained data results can be fully obtained according to the data in the parameter training set.

[0083] In some embodiments, information labels about parameter categories may also be set for all parameter training sets, so that efficient identification of parameter categories in the parameter training sets can be achieved through the use of the labels.

[0084] In some embodiments, the data in the parameter training set are recorded separately, and information labels are set for the parameter training set, so that in the subsequent parameter screening process, a method can be established in which a parameter in the first parameter training set corresponds to all parameters in other parameter training sets, so that in the final parameter analysis of the transfer function, set processing of all parameter types can be achieved, and different data groups can be obtained, and then appropriate parameter data groups can be screened out.

[0085] In the process of grouping the set parameters to obtain different parameter training sets, the technical solution includes:

[0086] The parameter categories are classified according to the proportional gain, integral gain, differential gain, integral term order, and differential term order.

[0087] Among them, the parameter categories mentioned above are confirmed based on the transfer function of the intelligent fractional-order controller, that is: the purpose of this application is to confirm all parameters in the transfer function, and in the subsequent processing process, all the data therein need to be processed, and finally in the transfer function, the independent variable is only the control signal.

[0088] In the process of classifying the data according to the corresponding parameters based on the parameter categories, it is necessary to construct a parameter training set for the data.

[0089] After the parameters are classified, it is necessary to set all the parameters involved in the entire processing process. This application adopts a solution of performing precise settings after the initial settings. Among them, for the initial settings, specifically:

[0090] The classified parameters are initially set to obtain initial setting parameters, wherein the initial setting is determined according to the maximum value and the minimum value of the parameter category.

[0091] Among them, all parameters in the intelligent fractional-order controller have a certain parameter range, which can be obtained based on experiments or related control fields. Therefore, in the specific processing, for all parameters, the maximum and minimum values ​​need to be obtained.

[0092] The maximum and minimum values ​​that have been obtained cannot be used directly in data training. Instead, all the data in them need to be filled to obtain more data, which involves filling in other values ​​within the maximum and minimum value range that have been obtained. Specifically:

[0093] An increase or decrease step size is set for the initial parameters, and the initial parameters are increased or decreased according to the increase or decrease step size to obtain a preset parameter sequence.

[0094] Among them, for all categories of parameters, it is necessary to set the increase or decrease step size for the parameters, and increase or decrease the parameters according to the set step size, so that the maximum and minimum value ranges that have been obtained can be filled with data.

[0095] The setting of the increase or decrease step size is set according to the data processing accuracy. In specific operations, the higher the control accuracy requirement of the intelligent fractional-order controller, the more data needs to be filled in the maximum and minimum value ranges, and the smaller the step size is set.

[0096] In some embodiments, the number of data filled in the maximum and minimum value intervals is processed in a specific number manner, such as: the number to be filled is n, in this case, the step size is set as follows:

[0097]

[0098] Among them, l represents the step length, M represents the maximum value, m represents the minimum value, and n represents the number of data that needs to be filled. After obtaining the step length value, the maximum value or the minimum value is increased or decreased.

[0099] In some embodiments, the number of data filled in the maximum and minimum numerical ranges is set algorithmically based on the accuracy of the final transfer function. In the specific technical solution processing, an adaptive setting of the number set therein can be achieved.

[0100] The preset parameter sequence refers to a data set obtained by sorting the maximum and minimum values ​​from large to small or from small to large, and without sorting all the data therein. It should be noted that all the data therein need to be able to be set to the corresponding parameter training set to strictly avoid data omission problems.

[0101] The preset parameter sequence is set to the corresponding classification data set to obtain a parameter training set.

[0102] Among them, for the parameter scheme set based on the set increase or decrease step size, it is necessary to be able to incorporate all the data generated therein into the corresponding classification data set in full accordance with the current data results and processing methods, and the classification data set is the training set.

[0103] After all the data are obtained and the parameter training set is defined, the parameters used in the parameter training set need to be screened, as described in step S130, specifically:

[0104] Performing algorithm optimization on the initial parameters in the training set includes:

[0105] Establish an adaptive optimization factor, the adaptive optimization factor equation is:

[0106]

[0107] Among them, α1 represents the maximum value of the adaptive optimization factor, α0 represents the minimum value of the adaptive optimization factor, and α i represents the adaptive optimization factor, F i represents the fitness of the particle, F gbest Represents the global minimum fitness, ABS() represents the absolute value, and MIN() represents the minimum value.

[0108] Among them, based on the obtained parameter training set, the maximum and minimum values ​​are obtained and calculated, and the fitness of the particles and the global minimum fitness are set, and then the calculation is performed.

[0109] Among them, all parameters in the parameter training set are selected, and the parameters therein are calculated, and then the calculation results for such parameters are obtained.

[0110] Among them, all obtained parameters need to be screened and verified. Only when the verification results meet the requirements can the current data results be considered applicable.

[0111] Among them, for all data results that meet the requirements, all parameters therein need to be included in the corresponding parameter set, so as to obtain parameter sets for different parameter categories.

[0112] After establishing the adaptive optimization factor, it is also necessary to confirm the particle position based on the adaptive optimization factor. The equation is:

[0113]

[0114] Among them, x i (t+1) represents the position of the particle at time (t+1), p i (t) represents the local attractor position of the particle at time t, α i represents the adaptive optimization factor, C(t) represents the center point position, x i (t) represents the position of the particle at time t, g represents a random number with Gaussian probability distribution, and k represents the value interval number.

[0115] Among them, after setting the adaptive optimization factor with a certain particle, that is, parameter, it is necessary to calculate the other particles therein to determine the particle position therein and obtain the result based on the particle position.

[0116] Among them, it is necessary to set Gaussian probability distribution random numbers to calculate the results.

[0117] Among them, after Ai determines that the processed data meets the screening requirements, it is necessary to update the particle position, specifically:

[0118] Based on the position result of the particle at time (t+1), the optimal position of the particle is updated.

[0119] Among them, for the particle position at time (t+1) that has passed the verification, it is necessary to use the particle position at that time to replace the particle position in the parameter data set that does not meet the optimization requirements, so as to achieve the update of the optimal position of the particle.

[0120] After updating the optimal position of the particle, it is necessary to obtain the parameters corresponding to the particle, specifically:

[0121] Obtain the particle parameters corresponding to the optimal position of the particle.

[0122] Among them, for the updated data, the parameters corresponding to the particle should be recorded to obtain the optimization result.

[0123] The data that has been obtained needs to be recorded, specifically:

[0124] Based on the parameter category, the particle parameters are set into a parameter set established based on the parameter category.

[0125] Among them, multiple parameter sets are established, and for the data corresponding to such parameter sets, data of the same type needs to be included in the parameter sets.

[0126] In some embodiments, it is necessary to set a parameter type tag for the parameter set so that in subsequent parameter screening, results are obtained based on the parameter type tag.

[0127] In the above steps of the present application, all parameters in the transfer function can be screened for optimization, but this screening method is based on the data type to obtain the result. Therefore, in the construction of the transfer function, it is also necessary to construct the transfer function based on the parameter category in the transfer function, and then verify it. Specifically, as described in step S140, it includes:

[0128] All the parameter combinations in the different parameter sets are obtained to obtain a data group.

[0129] Among them, the current parameter function is obtained from the parameter set. Considering that all parameters come from different parameter sets, in each parameter acquisition, a parameter needs to be obtained from each data set to generate a data group. The data group is expressed in the form of: [K p , K i , K d , λ, μ].

[0130] Among them, considering that each parameter set contains multiple parameters, we can take a certain parameter set as the basis and a certain parameter in it as the basis, and then randomly select a parameter from other parameter sets each time to obtain a data group. It should be noted that all the data groups need to be able to obtain all the data groups.

[0131] In some embodiments, a parameter set with the least number of parameters is obtained, and based on this parameter set, all data groups that can be obtained in all parameter sets are obtained. Based on this method, the workload in the process of obtaining data groups can be substantially reduced.

[0132] After obtaining the data set, the data in the data set needs to be set into the transfer function of the intelligent fractional-order controller for subsequent verification. Specifically:

[0133] The parameters in the data group are set to the transfer function of the intelligent fractional-order controller to obtain the transfer function of the intelligent fractional-order controller.

[0134] Wherein, based on the parameter types contained in the data group, the parameters therein are set to the transfer function of the intelligent fractional-order controller to obtain a transfer function with determined parameters.

[0135] After the transfer function of the intelligent fractional-order controller has been obtained, since the parameters in the obtained data set are randomly distributed, it cannot be guaranteed that the transfer function can meet the control requirements of the BLDCM. Therefore, further verification of the transfer function is required. Specifically:

[0136] A control signal is input to the intelligent fractional-order controller, and an actual operating parameter of the BLDCM corresponding to the control signal is obtained.

[0137] In the technical solution of the present application, a control signal is input to the intelligent fractional-order controller, and the operating parameters of the BLDCM are recorded under the currently set transfer function.

[0138] After obtaining the operating parameters of the BLDCM, it is necessary to further determine the control effect achieved. Specifically:

[0139] Based on the control signal, an expected operating parameter of the BLDCM corresponding to the control signal is obtained.

[0140] Among them, based on the control function and the model of BLDCM, the control parameters of BLDCM can also be determined based on various models therein.

[0141] After obtaining the expected operating parameters and the actual operating parameters, the obtained transfer function can be determined, specifically:

[0142] An error rate between the actual operating parameter of the BLDCM and the expected operating parameter of the BLDCM is calculated, and if the error rate is within an error rate threshold, the parameters in the parameter set are set in the intelligent fractional order controller transfer function.

[0143] Among them, the error rate of the actual operating parameters and the theoretical operating parameters is calculated, and the calculated error rate is compared with the allowable error rate. When it is found that the obtained error rate is within the allowable range, it can be considered that the transfer function parameters currently used meet the control requirements of the BLDCM, and the transfer function can be applied to the actual control of the BLDCM.

[0144] After obtaining the transfer function of the intelligent fractional-order controller, it is necessary to apply the intelligent fractional-order controller to the actual control process of the BLDCM to control the BLDCM. Specifically:

[0145] Based on the parameter set, establishing the intelligent fractional-order controller transfer function;

[0146] Sending control parameters to the intelligent fractional-order controller to obtain a control signal;

[0147] Based on the control signal, the BLDCM is controlled.

[0148] Among them, the parameters of the acquired intelligent fractional-order controller transfer function are set so that it can be applied to the specific control process of BLDCM.

[0149] The corresponding control parameters are sent to the intelligent fractional-order controller, so that the intelligent fractional-order controller processes the received control parameters and outputs the control results to the BLDCM, so that the BLDCM can operate based on the control signal.

[0150] In some embodiments, all acquired data sets are recorded so that during the control process of the BLDCM, the acquired transfer function can be adjusted based on specific control requirements of the BLDCM.

[0151] In some embodiments, the operating parameters of the BLDCM are set, so that in specific processing, corresponding data groups can be selected within different operating parameter intervals, and the parameters in the data groups can be set into the transfer function to perform interval adjustment.

[0152] The technical solution of the present application has a better control effect than the traditional control method. Figure 2 As shown in FIG. 1 , the BLDCM speed control result diagram based on the PID algorithm and the ASP-PID algorithm provided in the embodiment of the present application is shown. Figure 2 As shown in (a), in the PID control scheme, it can be found that the motor has an obvious prominent peak. When the motor speed is 2000r / min, the speed of BLDCM will exceed 2000r / min for a period of time, and then fall back to 2000r / min. Obviously, this control method cannot meet the high-precision control requirements for BLDCM. Figure 2 As shown in (b), under the ASP-PID control algorithm, when the speed of BLDCM reaches 2000 r / min, it takes a longer time to reach this speed, so the convergence speed is slower.

[0153] like Figure 3 As shown, a fitness value convergence curve diagram of a BLDCM control method based on an intelligent fractional-order controller provided in an embodiment of the present application shows that, after 20-25 data iterations of the parameters therein, the optimal individual fitness value has met the requirements of the control accuracy and adaptability of the BLDCM, and compared with the various parameter screening algorithms currently available, the processing efficiency has been greatly improved.

[0154] The beneficial effects of this application include:

[0155] 1. High-quality screening of intelligent fractional-order controller parameters is achieved. For an intelligent fractional-order controller to be able to play a good control role, it is obviously necessary to control based on a control transfer function. In this application, based on relevant algorithms, parameter sets of multiple transfer functions are obtained and the parameter sets are verified to obtain more accurate parameter results.

[0156] 2. Improve control accuracy. The operating accuracy of BLDCM depends on the controller. The control is based on the intelligent fractional-order controller. Since the controller introduces integral order and differential order, the system can have better control accuracy and flexibility. The intelligent algorithm is combined with the intelligent fractional-order controller to further improve the adaptive ability of the intelligent fractional-order controller, thereby improving the control accuracy of BLDCM.

[0157] 3. Possessing adaptive capability. The intelligent fractional-order controller and the intelligent algorithm in this application are processed in a coordinated manner. In this way, the relevant parameters in the intelligent fractional-order controller can be adaptively adjusted, and multiple parameter sets can be generated, so that the parameters can be adaptively adjusted based on the control requirements, so that the intelligent fractional-order controller has adaptive capability.

[0158] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to computer program instructions, and the aforementioned computer program can be stored in a non-volatile storage medium. When the computer program is executed, it executes the steps of the above method embodiments. Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a non-volatile storage medium and includes a number of instructions for enabling an electronic device (which can be a personal computer, a server, a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention.

[0159] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A BLDCM control method based on an intelligent fractional-order controller, characterized in that: The control method comprises: Establishing a transfer function of an intelligent fractional-order controller for controlling the BLDCM; Obtaining parameters in the transfer function of the intelligent fractional-order controller and establishing a training set; Based on the training set, determining a parameter set in a transfer function of the intelligent fractional-order controller; Based on the parameter set, determining a transfer function of the intelligent fractional-order controller; The BLDCM is controlled based on the intelligent fractional-order controller transfer function and control parameters.

2. The BLDCM control method based on intelligent fractional-order controller according to claim 1 is characterized in that: The step of establishing an intelligent fractional-order controller transfer function for controlling the BLDCM includes: The transfer function of the intelligent fractional-order controller is obtained. The transfer function of the intelligent fractional-order controller is: G c (s)=K p +K i s -λ +K d s μ Among them, G c (s) represents the output of the transfer function; K p Represents proportional gain; K i Indicates the integral gain; K d represents the differential gain; λ represents the order of the integral term; μ represents the order of the differential term; s represents the complex frequency domain variable in the Laplace transform; The BLDCM is controlled based on the transfer function output of the intelligent fractional-order controller.

3. The BLDCM control method based on intelligent fractional-order controller according to claim 1 is characterized in that: The step of obtaining the parameters in the transfer function of the intelligent fractional-order controller and establishing a training set includes: Setting parameters in the transfer function of the intelligent fractional-order controller to obtain setting parameters; The setting parameters are set to different training sets according to parameter categories to obtain different parameter training sets.

4. The BLDCM control method based on intelligent fractional-order controller according to claim 3 is characterized in that: The setting parameters are set to different training sets according to parameter categories to obtain different parameter training sets, including: The parameter categories are classified according to the proportional gain, integral gain, differential gain, integral term order, and differential term order; Initially setting the classified parameters to obtain initial setting parameters, wherein the initial setting is determined according to the maximum value and the minimum value of the parameter category; Setting an increase or decrease step length for the initial parameters, performing increase or decrease processing on the initial parameters according to the increase or decrease step length, and obtaining a preset parameter sequence; The preset parameter sequence is set to the corresponding classification data set to obtain a parameter training set.

5. The BLDCM control method based on intelligent fractional-order controller according to claim 1, characterized in that: The step of determining a parameter set in a transfer function of the intelligent fractional-order controller based on the parameter training set includes: Performing algorithm optimization on the initial parameters in the training set includes: Establish an adaptive optimization factor, the adaptive optimization factor equation is: Among them, α1 represents the maximum value of the adaptive optimization factor, α0 represents the minimum value of the adaptive optimization factor, and α i represents the adaptive optimization factor, F i represents the fitness of the particle, F gbest Represents the global minimum fitness, ABS() represents the absolute value, and MIN() represents the minimum value; Based on the adaptive optimization factor, the particle position is determined, and the equation is: Among them, x i (t+1) represents the position of the particle at time (t+1), p i (t) represents the local attractor position of the particle at time t, α i represents the adaptive optimization factor, C(t) represents the center point position, x i (t) represents the position of the particle at time t, g represents a random number with Gaussian probability distribution, and k represents the value interval number; Based on the position result of the particle at time (t+1), updating the optimal position of the particle; Obtaining the particle parameters corresponding to the optimal position of the particle; Based on the parameter category, the particle parameters are set into a parameter set established based on the parameter category.

6. The BLDCM control method based on intelligent fractional-order controller according to claim 1 is characterized in that: The step of determining the intelligent fractional-order controller transfer function based on the parameter set includes: Acquire all the parameter combinations in different parameter sets to obtain a data group; The parameters in the data group are set to the transfer function of the intelligent fractional-order controller to obtain the transfer function of the intelligent fractional-order controller.

7. The BLDCM control method based on intelligent fractional-order controller according to claim 6 is characterized in that: Also includes: Input a control signal to the intelligent fractional-order controller, and obtain actual operating parameters of the BLDCM corresponding to the control signal; Based on the control signal, obtaining the expected operating parameters of the BLDCM corresponding to the control signal; An error rate between the actual operating parameter of the BLDCM and the expected operating parameter of the BLDCM is calculated, and if the error rate is within an error rate threshold, the parameters in the parameter set are set in the intelligent fractional order controller transfer function.

8. The BLDCM control method based on intelligent fractional-order controller according to claim 1, characterized in that: The controlling of the BLDCM based on the intelligent fractional-order controller transfer function and control parameters includes: Based on the parameter set, establishing the intelligent fractional-order controller transfer function; Sending control parameters to the intelligent fractional-order controller to obtain a control signal; Based on the control signal, the BLDCM is controlled.

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