High-performance speed control parameter optimization and self-adjustment method for switched reluctance motors under variable speed and load conditions

Abstract

This invention discloses a high-performance speed control parameter optimization and self-adjustment method for switched reluctance motors under variable speed and load conditions, comprising the following steps: constructing a mathematical model between the optimization object and the optimization target; obtaining n sets of load torque, speed, and acceleration data; using a reinforcement learning algorithm to obtain the corresponding optimal control parameters; using a numerical fitting method to obtain the functional relationships between each optimal control parameter and the load torque, speed, and acceleration; obtaining the optimal control parameters under the current operating condition; and implementing control of the switched reluctance motor based on the obtained optimal control parameters to achieve optimal control of the switched reluctance motor throughout the entire process under variable speed and load conditions. This invention effectively improves the dynamic and steady-state performance of switched reluctance motors under variable speed and load conditions, and has the advantages of simple algorithm and easy implementation.

Description

Technical Field

[0001] This invention relates to the field of speed control of switched reluctance motors, and in particular to a method for optimizing and self-adjusting high-performance speed control parameters of switched reluctance motors under variable speed and load conditions. Background Technology

[0002] Switched reluctance motors (SRMs) are a new type of motor with advantages such as simple and robust structure, low starting current, high starting torque, and high efficiency. They are currently widely used in many fields, including electric vehicles and mining locomotives. To achieve high-performance speed control of SRMs, it is necessary not only to study effective speed control methods but also to obtain optimal control parameters in real time based on changes in load conditions. Only in this way can the SRM speed control system achieve its best technical performance.

[0003] Extensive research has been conducted on speed control methods and parameter optimization for switched reluctance motors (SRMs). Addressing the issue that fixed control parameters are insufficient for achieving optimal control performance in SRMs operating under variable speed and acceleration modes, a patent document (CN119519522A) proposes an optimization method that adjusts optimal control parameters in real-time based on changes in motor speed and acceleration, achieving high-performance speed control of SRMs under variable speed and acceleration modes. However, in certain applications, such as active heave compensation control systems for marine winches, the load operates not only under variable speed and acceleration modes but also under conditions of real-time load changes. These real-time load variations directly impact the motor's speed control performance. The aforementioned parameter optimization methods based on stable loads are insufficient. Therefore, achieving optimal control across the entire process under the coupling of multiple factors—variable speed, variable acceleration, and variable load—is a pressing issue. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a simple and easy-to-implement method for optimizing and self-adjusting the high-performance speed control parameters of a switched reluctance motor under variable speed and load conditions.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is: a high-performance speed control parameter optimization and self-adjustment method for switched reluctance motors under variable speed and load conditions, comprising the following steps:

[0006] S1: Taking the control parameters of the switched reluctance motor speed regulation system based on the super spiral control algorithm as the optimization object, and the operating efficiency, torque ripple coefficient and steady-state control accuracy of the switched reluctance motor as the optimization objectives, a mathematical model between the optimization object and the optimization objectives is constructed.

[0007] S2: Determine the range of load torque, speed and acceleration variation based on the technical specifications and operating conditions of the switched reluctance motor, and randomly select n sets of load torque, speed and acceleration data at equal intervals within the range of variation;

[0008] S3: For each set of selected load torque, speed and acceleration data, based on the established mathematical model between the optimization object and the optimization objective, a reinforcement learning algorithm is used to obtain the optimal control parameters corresponding to each set of load torque, speed and acceleration.

[0009] S4: Based on the obtained n sets of optimal control parameters and the corresponding load torque, speed and acceleration data, numerical fitting methods are used to obtain the functional relationships between each optimal control parameter and load torque, speed and acceleration;

[0010] S5: Real-time acquisition of load torque, speed and acceleration of the switched reluctance motor during actual operation, and obtaining the optimal control parameters under the current operating conditions based on the obtained functional relationship;

[0011] S6: Implement control of the switched reluctance motor based on the obtained optimal control parameters to achieve optimal control of the switched reluctance motor throughout the entire process under variable speed and load conditions.

[0012] The above-mentioned high-performance speed control parameter optimization and self-adjustment method for switched reluctance motors under variable speed and load conditions, in step S1, the basic principle of the switched reluctance motor speed control system based on the super-spiral control algorithm is as follows: the switched reluctance motor speed control system includes a speed control outer loop and a torque control inner loop, wherein the speed control outer loop controls the actual speed of the switched reluctance motor by... oh With the given speed of the switched reluctance motor The speed deviation Δ of the switched reluctance motor was obtained by comparison. oh Δ oh After processing by the superspiral control algorithm, the reference torque of the inner loop of torque control is obtained. Specifically:

[0013]

[0014] In the formula, T L This refers to the load torque of the switched reluctance motor. J and B These represent the moment of inertia and the coefficient of friction of the switched reluctance motor, Δ. oh Δ represents the speed deviation of the switched reluctance motor. oh' For Δ oh The first derivative, oh' for oh The derivative, t For time, () ′This indicates the derivative of the function within the parentheses; tanh() is the hyperbolic tangent function. c , d , α , β These represent the sliding surface coefficients of the superspiral control algorithm, the curvature gain of the hyperbolic tangent function of the superspiral control algorithm, the power-term convergence gain of the superspiral control algorithm, and the integral-term convergence of the superspiral control algorithm; among which... α , β The value of satisfies the following constraints:

[0015]

[0016] The torque control inner loop is based on the speed control outer loop. ,Will With the actual torque of the switched reluctance motor The torque deviation was obtained by comparison. , After processing by the direct torque control algorithm, the control signal of the corresponding power switch in the power conversion circuit is obtained. The power switch is controlled according to the obtained control signal to realize the tracking of the actual speed of the switched reluctance motor to the time-varying given speed.

[0017] The above-mentioned high-performance speed control parameter optimization and self-adjustment method for switched reluctance motors under variable speed and load conditions, in step S1, includes the sliding surface coefficient of the superspiral control algorithm as the optimization object. c The power-order approach rate gain of the superspiral control algorithm α and the curvature gain of the hyperbolic tangent function in the superspiral control algorithm d The optimization objectives include the operating efficiency, torque ripple coefficient, and steady-state control accuracy of the switched reluctance motor.

[0018] Operating efficiency of switched reluctance motor or The calculation formula is:

[0019]

[0020] In the formula, U k , i k , oh , m and t r These represent the phase voltage, phase current, actual speed, number of phases, and rotor pole pitch of the switched reluctance motor, respectively.

[0021] Torque ripple coefficient of switched reluctance motor e The calculation formula is:

[0022]

[0023] In the formula, T max and T min These represent the maximum and minimum torques of the switched reluctance motor, respectively. T ave This represents the average torque of the switched reluctance motor.

[0024] Steady-state control accuracy of switched reluctance motor The calculation formula is:

[0025]

[0026] In the formula, For the given speed of the switched reluctance motor, Δ oh i For the first i The deviation between the actual speed and the given speed of the switched reluctance motor collected in this second sampling;

[0027] Therefore, the mathematical model between the optimization object and the optimization objective is as follows:

[0028]

[0029] Constructing a multi-objective optimization fitness function F Specifically:

[0030]

[0031] In the formula, k 1. k 2. k 3 represents the weighting coefficients for operating efficiency, torque ripple coefficient, and steady-state control accuracy, respectively. k 1+ k 2 + k 3 = 1.

[0032] The above-mentioned high-performance speed control parameter optimization and self-adjustment method for switched reluctance motors under variable speed and load conditions, specifically step S3, involves using a reinforcement learning algorithm to obtain the optimal control parameters corresponding to each group of load torque, speed, and acceleration as follows:

[0033] Set relevant parameters, including the number of optimization objects and the maximum number of iterations. T Population size I Optimize the range of values ​​for objects. UB j , LB j They represent the first j The upper and lower limits of the value range for each optimization object. j=1, 2, 3 respectively correspond to the optimization objects c , α , d ;

[0034] S 32 : Determine the learning status and learning priorities of the first-generation learners;

[0035] S 33 The fitness function is used to calculate and compare the fitness values ​​of each individual in the first generation of learners. The minimum fitness value and its corresponding learning state are retained, and the retained learning state is recorded as the optimal state. x best [ j ] is the corresponding number j The optimal state of learners for each optimization object is determined, and learners for each optimization object are sorted in descending order of learning priority.

[0036] S 34 : Update the learning status and learning priorities of the next generation of learners;

[0037] S 35 Arrange the learning priorities of each learner in descending order;

[0038] S 36 Comparison step S 34 If the minimum fitness value of the obtained learner is less than the minimum fitness value to be retained, then update the minimum fitness value to be retained and the corresponding learner state; otherwise, the minimum fitness value to be retained and the corresponding learner state remain unchanged.

[0039] S 37 : Determine if the maximum number of iterations has been reached. T If the target is not reached, increment the iteration count by 1 and return to step S. 34 Otherwise, output the optimal learner state and calculate the optimal value for each optimization object.

[0040] The above-mentioned method for optimizing and self-adjusting high-performance speed control parameters of switched reluctance motors under variable speed and load conditions, wherein step S... 32 In this context, the formulas for calculating the learning status and learning priority of first-generation learners are as follows:

[0041]

[0042] In the formula, x i，0 [j] For the first generation j The first optimization object i The learning status of each learner i =1,2…… I, schedule i，0 [ j [The first generation] j The first optimization object i Learning priorities for each learner rand () represents a random number between 0 and 1.

[0043] The above-mentioned method for optimizing and self-adjusting high-performance speed control parameters of switched reluctance motors under variable speed and load conditions, wherein step S... 34 In China, the formula for updating the learning status and learning priorities of the next generation of learners is:

[0044]

[0045] In the formula, x i,d [ j ] indicates the first d The generation j The optimization object is the first i The learning status of each learner Indicates the sorted number of... d -1st generation j The optimization object is the first i Learning priorities for each learner schedule i,d [ j ] indicates the first d The generation j The optimization object is the first i Learning priorities for each learner Indicates the first d The generation j The optimization object is the first i The fitness function value corresponding to each learner. l Thresholds are adaptively selected to prioritize individual learning.

[0046] The above-mentioned method for optimizing and self-adjusting high-performance speed control parameters of switched reluctance motors under variable speed and load conditions, wherein step S... 34 middle, l The expression is:

[0047]

[0048] The above-mentioned method for optimizing and self-adjusting high-performance speed control parameters of switched reluctance motors under variable speed and load conditions, wherein step S... 37 In the above, the optimal value for each optimization object is:

[0049] .

[0050] In the above-mentioned high-performance speed control parameter optimization and self-adjustment method for switched reluctance motors under variable speed and load conditions, step S4 involves obtaining the functional relationships between each optimal control parameter and load torque, speed, and acceleration using a numerical fitting method, as detailed below:

[0051]

[0052] In the formula, A 0、 A 1. A 2. A 3. A 4. B 0、 B 1. B 2. B 3. B 4. C 0、 C 1. C 2. C 3. C 4. C 5. C 6. C 7. C All 8 are constant coefficients.

[0053] The beneficial effects of this invention are as follows:

[0054] 1. This invention addresses the speed control system of a switched reluctance motor based on a super-spiral control algorithm. It proposes to use a reinforcement learning algorithm to perform multi-objective optimization of the control parameters to obtain the corresponding optimal control parameters. This enables the speed control system to achieve the best operating efficiency, torque ripple coefficient, and steady-state control accuracy under any load torque, speed, and acceleration.

[0055] 2. This invention determines the functional relationships between the optimal control parameters of the switched reluctance motor speed control system based on the super spiral control algorithm and the motor speed, acceleration, and load torque. Based on these functional relationships and by collecting the actual speed, acceleration, and load torque of the switched reluctance motor in real time, the optimal control parameters under the current operating conditions can be obtained. This enables the optimal control of the speed control system under complex operating conditions involving multiple factors such as variable speed, variable acceleration, and variable load. Attached Figure Description

[0056] Figure 1 This is the overall flowchart of the present invention.

[0057] Figure 2 This is a block diagram illustrating the principle of the switched reluctance motor speed control system based on the superspiral control algorithm of the present invention. Detailed Implementation

[0058] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0059] like Figure 1 As shown, a high-performance speed control parameter optimization and self-adjustment method for switched reluctance motors under variable speed and load conditions includes the following steps:

[0060] S1: Taking the control parameters of the switched reluctance motor speed regulation system based on the super spiral control algorithm as the optimization object, and the operating efficiency, torque ripple coefficient and steady-state control accuracy of the switched reluctance motor as the optimization objectives, a mathematical model between the optimization object and the optimization objectives is constructed.

[0061] like Figure 2 As shown, the basic principle of the switched reluctance motor speed control system based on the superspiral control algorithm is as follows: The switched reluctance motor speed control system includes an outer loop for speed control and an inner loop for torque control. The outer loop for speed control measures the actual speed of the switched reluctance motor. oh With the given speed of the switched reluctance motor The speed deviation of the switched reluctance motor was compared. Give , Give After processing by the superspiral control algorithm, the reference torque of the inner loop of torque control is obtained. Specifically:

[0062]

[0063] In the formula, T L This refers to the load torque of the switched reluctance motor. J and B These represent the moment of inertia and the coefficient of friction of the switched reluctance motor, Δ. oh Δ represents the speed deviation of the switched reluctance motor. oh' For Δ oh The first derivative, oh' for oh The derivative, t For time, () ′ This indicates the derivative of the function within the parentheses; tanh() is the hyperbolic tangent function. c , d , α , β These represent the sliding surface coefficients of the superspiral control algorithm, the curvature gain of the hyperbolic tangent function of the superspiral control algorithm, the power-term convergence gain of the superspiral control algorithm, and the integral-term convergence of the superspiral control algorithm; among which... α , β The value of satisfies the following constraints:

[0064]

[0065] The torque control inner loop is based on the speed control outer loop. ,Will With the actual torque of the switched reluctance motor The torque deviation was obtained by comparison. , After processing by the direct torque control algorithm, the control signal of the corresponding power switch in the power conversion circuit is obtained. The power switch is controlled according to the obtained control signal to realize the tracking of the actual speed of the switched reluctance motor to the time-varying given speed.

[0066] The optimization targets include the sliding surface coefficients of the superspiral control algorithm. c The power-order approach rate gain of the superspiral control algorithm α and the curvature gain of the hyperbolic tangent function in the superspiral control algorithm d The optimization objectives include the operating efficiency, torque ripple coefficient, and steady-state control accuracy of the switched reluctance motor.

[0067] Operating efficiency of switched reluctance motor or The calculation formula is:

[0068]

[0069] In the formula, U k , i k , oh , m and t r These represent the phase voltage, phase current, actual speed, number of phases, and rotor pole pitch of the switched reluctance motor, respectively.

[0070] Torque ripple coefficient of switched reluctance motor e The calculation formula is:

[0071]

[0072] In the formula, T max and T min These represent the maximum and minimum torques of the switched reluctance motor, respectively. T ave This represents the average torque of the switched reluctance motor.

[0073] Steady-state control accuracy of switched reluctance motor The calculation formula is:

[0074]

[0075] In the formula, For the given speed of the switched reluctance motor, Δ oh i For the first i The deviation between the actual speed and the given speed of the switched reluctance motor collected in this second sampling;

[0076] Therefore, the mathematical model between the optimization object and the optimization objective is as follows:

[0077]

[0078] Constructing a multi-objective optimization fitness function F Specifically:

[0079]

[0080] In the formula, k 1. k 2. k 3 represents the weighting coefficients for operating efficiency, torque ripple coefficient, and steady-state control accuracy, respectively. k 1+ k 2 + k 3 = 1.

[0081] S2: Determine the range of variation for load torque, speed, and acceleration based on the technical specifications and operating conditions of the switched reluctance motor, and arbitrarily select values ​​at equal intervals within the range of variation. n Group load torque, speed and acceleration data.

[0082] S3: For each set of selected load torque, speed and acceleration data, based on the established mathematical model between the optimization object and the optimization objective, a reinforcement learning algorithm is used to obtain the optimal control parameters corresponding to each set of load torque, speed and acceleration.

[0083] The specific steps for obtaining the optimal control parameters corresponding to each set of load torque, speed, and acceleration using reinforcement learning algorithms are as follows:

[0084] S 31 Set relevant parameters, including the number of objects to optimize and the maximum number of iterations. T Population size I Optimize the range of values ​​for objects. UB j , LB j They represent the first j The upper and lower limits of the value range for each optimization object. j =1, 2, 3 respectively correspond to the optimization objects c , α , d ;

[0085] S 32The learning status and learning priority of the first-generation learners are determined by the following formula:

[0086]

[0087] In the formula, x i，0 [j] For the first generation j The first optimization object i The learning status of each learner i =1,2…… I , schedule i，0 [ j [The first generation] j The first optimization object i Learning priorities for each learner rand () represents a random number between 0 and 1.

[0088] S 33 The fitness function is used to calculate and compare the fitness values ​​of each individual in the first generation of learners. The minimum fitness value and its corresponding learning state are retained, and the retained learning state is recorded as the optimal state. x best [ j ] is the corresponding number j The optimal state of learners for each optimization object is determined, and learners for each optimization object are sorted in descending order of learning priority.

[0089] S 34 The formula for updating the learning status and priorities of the next generation of learners is:

[0090]

[0091] In the formula, x i,d [ j ] indicates the first d The generation j The optimization object is the first i The learning status of each learner Indicates the sorted number of... d -1st generation j The optimization object is the first i Learning priorities for each learner schedule i,d [ j ] indicates the first d The generation j The optimization object is the first i Learning priorities for each learner Indicates the first d The generation j The optimization object is the firsti The fitness function value corresponding to each learner. l Adaptively select thresholds for individual learning priorities:

[0092]

[0093] S 35 Arrange the learning priorities of each learner in descending order;

[0094] S 36 Comparison step S 34 If the minimum fitness value of the obtained learner is less than the minimum fitness value to be retained, then update the minimum fitness value to be retained and the corresponding learner state; otherwise, the minimum fitness value to be retained and the corresponding learner state remain unchanged.

[0095] S 37 : Determine if the maximum number of iterations has been reached. T If the target is not reached, increment the iteration count by 1 and return to step S. 34 Otherwise, output the optimal learner state and calculate the optimal value for each optimization object;

[0096] The optimal values ​​for each optimization object are:

[0097] .

[0098] S4: Based on the results n The optimal control parameters and corresponding load torque, speed, and acceleration data were used to obtain the functional relationships between each optimal control parameter and load torque, speed, and acceleration using numerical fitting methods.

[0099] Numerical fitting methods were used to obtain the functional relationships between each optimal control parameter and load torque, speed, and acceleration, as follows:

[0100]

[0101] In the formula, A 0、 A 1. A 2. A 3. A 4. B 0、 B 1. B 2. B 3. B 4. C 0、 C 1. C 2. C 3. C 4. C 5. C6. C 7. C All 8 are constant coefficients.

[0102] S5: Real-time acquisition of load torque, speed and acceleration of the switched reluctance motor during actual operation, and obtaining the optimal control parameters under the current operating conditions based on the obtained functional relationship.

[0103] S6: Implement control of the switched reluctance motor based on the obtained optimal control parameters to achieve optimal control of the switched reluctance motor throughout the entire process under variable speed and load conditions.

[0104] To illustrate the effectiveness of the self-adjustment method for optimizing the speed control parameters of a switched reluctance motor under variable speed and load conditions provided by this invention, a 12 / 8-pole switched reluctance motor is used as an example for verification. Its main technical parameters are shown in Table 1.

[0105]

[0106] Based on the technical parameters shown in Table 1 and the operating conditions of the switched reluctance motor, assuming its rated load torque and maximum angular acceleration are 81... and 80 rad / s 2 And within its rated load torque, rated speed, and maximum angular acceleration range, if any 2 is chosen... 60 rpm, 10 rad / s 2 Starting from 2, and randomly selecting 2 35 rpm, 2 rad / s 2 Twenty sets of load torque, speed and acceleration data were selected at equal intervals. For each set of data, the corresponding optimal control parameters were obtained by using the control parameter optimization self-adjustment method provided by this invention, as shown in Table 2.

[0107]

[0108] Based on the optimal control parameter values ​​and corresponding load torque, motor speed, and acceleration data shown in Table 2, the functional relationships between each optimal parameter and load torque, motor speed, and acceleration are obtained using a numerical fitting method, as follows:

[0109]

[0110] Furthermore, to further illustrate the effectiveness of the control parameter optimization self-adjustment method (hereinafter referred to as the "method") provided by this invention, a comparative analysis is conducted between the method and the traditional fixed control parameter control method (hereinafter referred to as the "traditional method"). The control parameters used in the traditional method are the optimal control parameters obtained by a reinforcement learning algorithm for a switched reluctance motor under rated operating conditions, i.e., rated load torque, rated speed, and maximum acceleration, as shown in Table 3.

[0111]

[0112] Take any two sets of data within the rated load torque, rated speed, and maximum angular acceleration range of the switched reluctance motor. For example, take the motor load torque, speed, and acceleration as follows: 25 900 rpm, 20.94 rad / s 2 and 40 400 rpm, 10.47 rad / s 2 The switching reluctance motor speed control system was controlled using this method and the traditional method respectively, and its operating efficiency, torque ripple coefficient and steady-state control accuracy are shown in Table 4 and Table 5 respectively.

[0113]

[0114]

[0115] As shown in Tables 4 and 5, for any two sets of load torque, motor speed, and acceleration data, compared with the traditional method, the motor efficiency obtained by this method is increased by 1.12% and 0.45% respectively, the torque ripple coefficient is reduced by 22.12% and 28.23% respectively, and the steady-state control accuracy is improved by 6.86% and 7.56% respectively. This further proves that the switching reluctance motor speed control system effectively improves the control effect by using this method.

Claims

1. A method for optimizing and self-adjusting high-performance speed control parameters of a switched reluctance motor under variable speed and load conditions, characterized in that, Includes the following steps: S1: Taking the control parameters of the switched reluctance motor speed regulation system based on the super spiral control algorithm as the optimization object, and the operating efficiency, torque ripple coefficient and steady-state control accuracy of the switched reluctance motor as the optimization objectives, a mathematical model between the optimization object and the optimization objectives is constructed. S2: Determine the range of load torque, speed and acceleration variation based on the technical specifications and operating conditions of the switched reluctance motor, and randomly select n sets of load torque, speed and acceleration data at equal intervals within the range of variation; S3: For each set of selected load torque, speed and acceleration data, based on the established mathematical model between the optimization object and the optimization objective, a reinforcement learning algorithm is used to obtain the optimal control parameters corresponding to each set of load torque, speed and acceleration. In step S3, the specific steps for obtaining the optimal control parameters corresponding to each group of load torque, speed, and acceleration using the reinforcement learning algorithm are as follows: S 31 Set relevant parameters, including the number of objects to optimize and the maximum number of iterations. T Population size I Optimize the range of values ​​for objects. UB j , LB j They represent the first j The upper and lower limits of the value range for each optimization object. j =1, 2, 3 respectively correspond to the optimization objects c , α , δ ; S 32 : Determine the learning status and learning priorities of the first-generation learners; S 33 The fitness function is used to calculate and compare the fitness values ​​of each individual in the first generation of learners. The minimum fitness value and its corresponding learning state are retained, and the retained learning state is recorded as the optimal state. best [ j [This represents the optimal state of the learner corresponding to the j-th optimization object, and the learners of each optimization object are sorted in descending order of learning priority;] S 34 : Update the learning status and learning priorities of the next generation of learners; S 35 Arrange the learning priorities of each learner in descending order; S 36 Comparison step S 34 If the minimum fitness value of the obtained learner is less than the minimum fitness value to be retained, then update the minimum fitness value to be retained and the corresponding learner state; otherwise, the minimum fitness value to be retained and the corresponding learner state remain unchanged. S 37 : Determine if the maximum number of iterations has been reached. T If the target is not reached, increment the iteration count by 1 and return to step S. 34 Otherwise, output the optimal learner state and calculate the optimal value for each optimization object; S4: Based on the obtained n sets of optimal control parameters and the corresponding load torque, speed and acceleration data, numerical fitting methods are used to obtain the functional relationships between each optimal control parameter and load torque, speed and acceleration; S5: Real-time acquisition of load torque, speed and acceleration of the switched reluctance motor during actual operation, and obtaining the optimal control parameters under the current operating conditions based on the obtained functional relationship; S6: Implement control of the switched reluctance motor based on the obtained optimal control parameters to achieve optimal control of the switched reluctance motor throughout the entire process under variable speed and load conditions.

2. The method for optimizing and self-adjusting high-performance speed control parameters of a switched reluctance motor under variable speed and load conditions as described in claim 1, is characterized in that... In step S1, the basic principle of the switched reluctance motor speed control system based on the superspiral control algorithm is as follows: The switched reluctance motor speed control system includes an outer loop for speed control and an inner loop for torque control, wherein the outer loop for speed control measures the actual speed of the switched reluctance motor. ω With the given speed of the switched reluctance motor The speed deviation Δ of the switched reluctance motor was obtained by comparison. ω Δ ω After processing by the superspiral control algorithm, the reference torque of the inner loop of torque control is obtained. Specifically: ； In the formula, T L Let J be the load torque of the switched reluctance motor, and B be the moment of inertia and coefficient of friction of the switched reluctance motor, respectively. ω Δ represents the speed deviation of the switched reluctance motor. ω ′ is Δ ω The first derivative, ω 'for ω The derivative of t For time, ( )′ represents the derivative of the function within the parentheses, tanh( ) is the hyperbolic tangent function. c , δ , α , β These represent the sliding surface coefficients of the superspiral control algorithm, the curvature gain of the hyperbolic tangent function of the superspiral control algorithm, the power-term convergence gain of the superspiral control algorithm, and the integral-term convergence of the superspiral control algorithm; among which... α , β The value of satisfies the following constraints: ； The torque control inner loop is obtained from the speed control outer loop. ,Will With the actual torque of the switched reluctance motor T e The torque deviation Δ is obtained by comparison. T e Δ T e After processing by the direct torque control algorithm, the control signal of the corresponding power switch in the power conversion circuit is obtained. The power switch is controlled according to the obtained control signal to realize the tracking of the actual speed of the switched reluctance motor to the time-varying given speed.

3. The method for optimizing and self-adjusting high-performance speed control parameters of a switched reluctance motor under variable speed and load conditions as described in claim 2, is characterized in that... In step S1, the optimization objects include the sliding surface coefficient c of the superspiral control algorithm, the power term reaching rate gain α of the superspiral control algorithm, and the curvature gain of the hyperbolic tangent function of the superspiral control algorithm. δ The optimization objectives include the operating efficiency, torque ripple coefficient, and steady-state control accuracy of the switched reluctance motor. The formula for calculating the operating efficiency η of a switched reluctance motor is: ； In the formula, U k , i k , ω , m and τ r These represent the phase voltage, phase current, actual speed, number of phases, and rotor pole pitch of the switched reluctance motor, respectively. Torque ripple coefficient of switched reluctance motor ε The calculation formula is: ； In the formula, T max and T min These represent the maximum and minimum torques of the switched reluctance motor, respectively. T ave This represents the average torque of the switched reluctance motor. Steady-state control accuracy of switched reluctance motor The calculation formula is: ； In the formula, Δ is the given speed of the switched reluctance motor. ω i The deviation between the actual speed of the switched reluctance motor and the given speed is obtained in the i-th acquisition. Therefore, the mathematical model between the optimization object and the optimization objective is as follows: ； Construct a multi-objective optimization fitness function F, specifically as follows: ； In the formula, k 1. k 2. k 3 represents the weighting coefficients for operating efficiency, torque ripple coefficient, and steady-state control accuracy, respectively. k 1+ k 2 + k 3 = 1.

4. The method for optimizing and self-adjusting high-performance speed control parameters of a switched reluctance motor under variable speed and load conditions as described in claim 3, is characterized in that... The step S 32 In this context, the formulas for calculating the learning status and learning priority of first-generation learners are as follows: ； In the formula, x i，0 [ j] For the first generation j The first optimization object i The learning status of each learner i =1,2……I,schedule i，0 [ j] For the first generation j The first optimization object i The learning priority of each learner, and rand() is a random number between 0 and 1.

5. The method for optimizing and self-adjusting high-performance speed control parameters of a switched reluctance motor under variable speed and load conditions according to claim 4, characterized in that, The step S 34 In China, the formula for updating the learning status and learning priorities of the next generation of learners is: ; In the formula, x i,d [ j ] indicates the first d The generation j The optimization object is the first i The learning status of each learner Indicates the sorted number of... d -1st generation j The optimization object is the first i Learning priorities and schedules for individual learners i,d [ j ] indicates the dth generation. j The optimization object is the first i Learning priorities for each learner Let λ represent the fitness function value corresponding to the j-th optimization object and the i-th learner in the d-th generation, and let λ be the threshold for adaptive selection of individual learning priority.

6. The method for optimizing and self-adjusting high-performance speed control parameters of a switched reluctance motor under variable speed and load conditions as described in claim 5, is characterized in that... The step S 34 In this context, the expression for λ is: 。 7. The method for optimizing and self-adjusting high-performance speed control parameters of a switched reluctance motor under variable speed and load conditions as described in claim 6, is characterized in that... The step S 37 In the above, the optimal value for each optimization object is: 。 8. The method for optimizing and self-adjusting high-performance speed control parameters of a switched reluctance motor under variable speed and load conditions as described in claim 7, characterized in that: In step S4, numerical fitting methods are used to obtain the functional relationships between each optimal control parameter and load torque, speed, and acceleration, as follows: ； In the formula, A0, A1, A2, A3, A4, B0, B1, B2, B3, B4, C0, C1, C2, C3, C4, C5, C6, C7, and C8 are all constant coefficients.

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