High-performance speed control parameter optimization method for switched reluctance motor based on super-helical algorithm

Abstract

The present invention discloses a method for optimizing the high-performance speed control parameters of a switched reluctance motor based on a superhelical algorithm, which belongs to the technical field of switched reluctance motor speed control. The method comprises the following steps: constructing a mathematical model between the optimization object and the optimization target, constructing a multi-objective optimization fitness function based on the mathematical model; determining the speed and acceleration range according to the operating conditions, and taking any equal-spaced value within the above range. n The speed and acceleration data of the group are obtained; the optimal control parameters of the super spiral control are obtained by using the improved walking optimization algorithm; n The optimal control parameters and motor speed and acceleration data are set, and the functional relationship between the optimal control parameters and its speed and acceleration is obtained by numerical fitting method; the optimal control parameters are obtained according to the functional relationship and the speed and acceleration of the switched reluctance motor during operation; the operation of the switched reluctance motor is controlled according to the optimal control parameters, so as to realize the full-process high-performance speed regulation control of the switched reluctance motor under the variable acceleration mode.

Description

Technical Field

[0001] The invention belongs to the technical field of switched reluctance motor speed control, and in particular relates to a high-performance switched reluctance motor speed control parameter optimization method based on a super-helical algorithm. Background Art

[0002] The switched reluctance motor is a new type of motor with a series of advantages such as simple and solid structure, small starting current, large starting torque, and high efficiency. It has developed rapidly in recent years and has been widely used in many fields such as electric vehicles and mining electric locomotives. However, in order to achieve high-performance speed control of the switched reluctance motor, it is a basic prerequisite to study effective speed control methods to achieve the above control requirements.

[0003] At present, a lot of research has been carried out on the speed control of switched reluctance motors and a variety of control methods have been proposed. Although good results have been achieved, the existing control methods are all proposed for the motor under the operating conditions of basically stable loads. However, when the motor is in the operating conditions of variable speed and variable load, the existing control methods are difficult to meet the requirements. For this reason, the patent "High-performance speed control method for switched reluctance motors suitable for dynamic loads" (CN118353330B) proposes a speed control method based on a super-helical algorithm for switched reluctance motors based on variable speed and variable load conditions, and has achieved good results; however, when the motor speed and acceleration change, its related control parameters need to be optimized and adjusted accordingly, so that the switched reluctance motor can achieve the best operating effect. Therefore, if the optimal control parameters of the switched reluctance motor can be adjusted in real time according to its actual speed and acceleration, it is the key to achieve high-performance speed control of the switched reluctance motor in the whole process under the variable speed and variable acceleration mode. Summary of the invention

[0004] In order to solve the above technical problems existing in the prior art, the present invention provides a high-performance speed regulation control parameter optimization method for a switched reluctance motor based on a super-helical algorithm.

[0005] The technical solution of the present invention to solve the above technical problems is: a method for optimizing high-performance speed control parameters of a switched reluctance motor based on a super-helical algorithm, comprising the following steps:

[0006] S1, taking the super-helical control parameters as the optimization object, taking the torque ripple coefficient of the switched reluctance motor and its steady-state control accuracy as the optimization target, and constructing a mathematical model between the optimization object and the optimization target;

[0007] Its mathematical model is:

[0008] (5);

[0009] In the above formula, ε is the torque ripple coefficient of the switched reluctance motor. is the steady-state control accuracy of the switched reluctance motor, T max and T min Respectively represent the maximum torque and minimum torque of the switched reluctance motor, T ave is the average torque of the switched reluctance motor; is the given speed of the switched reluctance motor, Δω i is the deviation between the actual speed of the switched reluctance motor collected for the i-th time and its given speed;

[0010] S2, constructing its multi-objective optimization fitness function according to the mathematical model between the obtained optimization object and the optimization target;

[0011] S3, determining the speed and acceleration range of the switched reluctance motor according to its operating conditions, and randomly selecting n groups of speed and acceleration data at equal intervals within the above range;

[0012] S4, for each set of selected speed and acceleration data, an improved walking optimization algorithm is used to obtain the optimal value of each control parameter of the superhelical control;

[0013] S5, according to the obtained n groups of optimal control parameters and their corresponding motor speed and acceleration data, a numerical fitting method is used to obtain the functional relationship between each optimal control parameter and its speed and acceleration;

[0014] S6, obtaining the optimal control parameters under the speed and acceleration according to the obtained functional relationship of the optimal control parameters and the speed and acceleration of the switched reluctance motor during operation;

[0015] S7, controlling the switched reluctance motor according to the obtained optimal control parameters, and realizing full-process high-performance speed regulation control of the switched reluctance motor under the variable speed and variable acceleration mode.

[0016] Further: In step S2, according to the obtained mathematical model between the optimization object and the optimization target, a multi-objective optimization fitness function is constructed, which is specifically:

[0017] According to formula (5), the multi-objective optimization fitness function F is constructed as:

[0018] (6);

[0019] In the formula, k1 and k2 are weight coefficients, and k1+k2 =1.

[0020] Further: Step S4 adopts an improved walking optimization algorithm to obtain the optimal value of each control parameter of superhelical control, and the specific method is:

[0021] S 41, set relevant parameters: including the maximum number of iterations M, the number of hikers H, the hiking range L and the individual coordinates of the first generation of hikers. Among them, the individual coordinates of the first generation of hikers are randomly selected within the hiking range, specifically:

[0022] (7);

[0023] Where (x c1,0 , y c1,0 ), (x α1,0 , y α1,0 ), (x δ1,0 , y δ1,0 ) are the individual coordinates of the first generation hiker, and random() represents a random number between 0 and 1.

[0024] The individual coordinates of the i-th first-generation hiker are shown in formula (8), i=2,3...H;

[0025] (8);

[0026] Where (x ci,0 , y ci,0 ), (x αi,0 , y αi,0 ), (x δi,0 , y δi,0 ) are the individual coordinates of the i-th first-generation hiker, (x ci-1,0 , y ci-1,0 ), (x αi-1,0 , y αi-1,0 ), (x δi-1,0 , y δi-1,0 ) are the individual coordinates of the i-1th first-generation hiker, μ1 and μ2 are random values ​​in the range of (0,1).

[0027] S 42 , calculate and compare the fitness values ​​corresponding to each individual of the first generation of hikers according to formula (6), retain the minimum fitness value and its corresponding individual coordinates of the hiker, and mark the retained individual coordinates of the hiker as the leader coordinates (X c ,Y c )、(X α ,Y α ) and (X δ ,Y δ ).

[0028] S 43 , according to the leader's coordinates, update the next generation of hikers' individual coordinates. Among them, the jth hiker's individual coordinates are:

[0029] (9);

[0030] In the formula (θ cj,x , θ cj,y ), (θ αj,x , θ αj,y ), (θ δj,x , θ δj,y ) are random angles in [0, 50°] (j=1,2,3…H), m is the current generation, M is the maximum number of iterations, γ1 and γ2 are random numbers in (0,1), and S is an integer in [1,3].

[0031] S 44 , according to step S 43 The individual coordinates of the next generation of hikers are obtained, and the fitness values ​​corresponding to each individual of the next generation of hikers are calculated and compared using formula (6).

[0032] S 45 , comparison step S 44 Is the obtained minimum fitness value of the individual hiker less than the reserved minimum fitness value? If so, update the coordinates of the leader corresponding to the reserved minimum fitness value; otherwise, execute step S 46 .

[0033] S 46 , determine whether the number of iterations reaches the maximum number of iterations M. If not, add 1 to the number of iterations and return to step S 43 ; Otherwise, output the leader's coordinates and calculate the optimal values ​​of the optimization objects c, α, and δ, as follows:

[0034] (10);

[0035] Where X c and Y c Respectively represent the horizontal and vertical coordinates of the optimization object c, X α and Y α Respectively represent the horizontal and vertical coordinates of the optimization object α, X δ and Y δ They represent the horizontal and vertical coordinates of the optimization object δ respectively.

[0036] Further: the step S5 uses a numerical fitting method to obtain the functional relationship between each optimal control parameter and its rotation speed and acceleration, and its specific form is as follows:

[0037] (11);

[0038] Where ω is the actual speed of the switched reluctance motor, ω′ is the angular acceleration of the switched reluctance motor, and A0~A6, B0~B6, and C0~C6 are all constant coefficients.

[0039] The beneficial effects of the present invention are:

[0040] The present invention takes the control parameters related to the super-helical control algorithm as the optimization object, takes the torque pulsation coefficient of the switched reluctance motor and its steady-state control accuracy as the optimization target, constructs a mathematical model between the optimization object and the optimization target, and constructs its multi-objective optimization fitness function based on the mathematical model; determines the range of speed and acceleration changes according to the operating conditions of the switched reluctance motor, and randomly selects n groups of speed and acceleration data at equal intervals within the above range; for each group of speed and acceleration data selected, adopts the improved walking optimization algorithm to obtain the optimal control parameters of the super-helical control; according to the obtained n groups of optimal control parameters and their corresponding motor speed and acceleration data, adopts the numerical fitting method to obtain the functional relationship between each optimal control parameter and its speed and acceleration; according to the above-mentioned functional relationships and the speed and acceleration of the switched reluctance motor during operation, obtains the optimal control parameters under the speed and acceleration; controls the switched reluctance motor according to the obtained optimal control parameters, and can realize the full-process high-performance speed regulation control of the switched reluctance motor based on the variable speed and acceleration mode. The present invention effectively reduces the torque pulsation of the switched reluctance motor, improves its steady-state accuracy, and has the characteristics of simple algorithm and easy implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The principle block diagram of the switched reluctance motor speed control system based on the super-helical algorithm provided by the present invention;

[0042] Figure 2 is a flow chart of the present invention;

[0043] Figure 3 It is a flowchart of the walking optimization algorithm in the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0045] See also Figure 1 , Figure 1 The schematic diagram of the speed control system of the switched reluctance motor based on the super-helical algorithm in the present invention is shown in FIG. 1 , which includes an outer speed control loop and an inner torque control loop. The basic principle is:

[0046] (1);

[0047] Where T eis the actual torque of the switched reluctance motor, J and B are the moment of inertia and friction coefficient of the switched reluctance motor, Δω is the deviation between the actual speed of the switched reluctance motor and its given speed, Δω′ is the first-order derivative of the speed deviation, c, δ, α, β are the sliding surface coefficient of the superhelical algorithm, the hyperbolic tangent function curvature gain, the power term approach rate gain and the integral term approach rate, respectively. The values ​​of α and β must meet the following constraints:

[0048] (2);

[0049] The inner torque control loop controls the reference torque T obtained by the outer speed control loop. ref and compare it with the actual torque T of the switched reluctance motor e Compared with the deviation ΔT e After being processed by the direct torque control algorithm, a control signal of the corresponding power switch in its power conversion circuit is obtained; the corresponding power switch in its power conversion circuit is controlled according to the obtained control signal, so that the actual speed of the switched reluctance motor can accurately track its time-varying given speed.

[0050] See also Figure 2 and Figure 3 , respectively, are a flow chart of a high-performance speed control parameter optimization method for a switched reluctance motor based on a super-helical algorithm and a flow chart of a walking optimization algorithm provided by the present invention. The specific optimization steps are as follows:

[0051] S1, taking the super-helical control parameters as the optimization object, taking the switched reluctance motor torque ripple coefficient and its steady-state control accuracy as the optimization target, constructing the mathematical model between the optimization object and the optimization target, as follows:

[0052] The optimization objects include the sliding surface coefficient c, the power term approach rate gain α and the hyperbolic tangent function curvature gain δ of the super-helical control algorithm; the optimization target is the torque ripple coefficient and steady-state control accuracy of the switched reluctance motor. The specific mathematical model is:

[0053] The expression of the torque ripple coefficient ε of the switched reluctance motor is:

[0054] (3);

[0055] Where T max and T min Respectively represent the maximum torque and minimum torque of the switched reluctance motor, T ave is the average torque of the switched reluctance motor.

[0056] The calculation formula for the steady-state control accuracy of the switched reluctance motor is:

[0057] (4);

[0058] In the formula is the given speed of the switched reluctance motor, Δω i is the deviation between the actual speed of the switched reluctance motor collected for the i-th time and its given speed.

[0059] Formula (3) and formula (4) constitute the mathematical model between the optimization object and the optimization goal, namely:

[0060] (5);

[0061] S2, according to formula (5), constructs its multi-objective optimization fitness function F, specifically:

[0062] (6);

[0063] In the formula, k1 and k2 are weight coefficients, and k1+k2 =1.

[0064] S3, determining the speed and acceleration range of the switched reluctance motor according to its operating conditions, and randomly selecting n groups of speed and acceleration data at equal intervals within the above range;

[0065] S4, for each set of selected speed and acceleration data, the improved walking optimization algorithm is used to obtain the optimal value of each control parameter of the super spiral control. The specific steps are as follows:

[0066] S 41 , set relevant parameters: including the maximum number of iterations M, the number of hikers H, the hiking range L and the individual coordinates of the first generation of hikers. Among them, the individual coordinates of the first generation of hikers are randomly selected within the hiking range, specifically:

[0067] (7);

[0068] Where (x c1,0 , y c1,0 ), (x α1,0 , y α1,0 ), (x δ1,0 , y δ1,0 ) are the individual coordinates of the first generation hiker, and random() represents a random number between (0,1).

[0069] The individual coordinates of the i-th (i=2,3...H) first-generation hiker are given by formula (8):

[0070] (8);

[0071] Where (x ci,0 , y ci,0 ), (x αi,0 , yαi,0 ), (x δi,0 , y δi,0 ) are the individual coordinates of the i-th first-generation hiker, (x ci-1,0 , y ci-1,0 ), (x αi-1,0 , y αi-1,0 ), (x δi-1,0 , y δi-1,0 ) are the individual coordinates of the i-1th first-generation hiker, μ1 and μ2 are random values ​​in the range of (0,1).

[0072] S 42 , calculate and compare the fitness values ​​corresponding to each individual of the first generation of hikers according to formula (6), retain the minimum fitness value and its corresponding individual coordinates of the hiker, and mark the retained individual coordinates of the hiker as the leader coordinates (X c ,Y c )、(X α ,Y α ) and (X δ ,Y δ ).

[0073] S 43 , according to the leader's coordinates, update the next generation of hikers' individual coordinates. Among them, the jth hiker's individual coordinates are:

[0074] (9);

[0075] In the formula (θ cj,x , θ cj,y ), (θ αj,x , θ αj,y ), (θ δj,x , θ δj,y ) are random angles in [0, 50°] (j=1,2,3…H), m is the current generation, M is the maximum number of iterations, γ1 and γ2 are random numbers in (0,1), and S is an integer in [1,3].

[0076] S 44 , according to step S 43 The individual coordinates of the next generation of hikers are obtained, and the fitness values ​​corresponding to each individual of the next generation of hikers are calculated and compared using formula (6).

[0077] S 45 , comparison step S 44 Is the obtained minimum fitness value of the individual hiker less than the reserved minimum fitness value? If so, update the coordinates of the leader corresponding to the reserved minimum fitness value; otherwise, execute step S 46 .

[0078] S 46, determine whether the number of iterations reaches the maximum number of iterations M. If not, add 1 to the number of iterations and return to step S 43 ; Otherwise, output the leader's coordinates and calculate the optimal values ​​of the optimization objects c, α, and δ, as follows:

[0079] (10);

[0080] Where X c and Y c Respectively represent the horizontal and vertical coordinates of the optimization object c, X α and Y α Respectively represent the horizontal and vertical coordinates of the optimization object α, X δ and Y δ They represent the horizontal and vertical coordinates of the optimization object δ respectively.

[0081] S5, based on the obtained n groups of optimal control parameters and their corresponding motor speed and acceleration data, a numerical fitting method is used to obtain the functional relationship between each optimal control parameter and its speed and acceleration, and its specific form is as follows:

[0082] (11);

[0083] Where ω is the actual speed of the switched reluctance motor, ω′ is the angular acceleration of the switched reluctance motor, and A0~A6, B0~B6, and C0~C6 are all constant coefficients.

[0084] S6, obtaining the optimal control parameters under the speed and acceleration according to the functional relationships obtained above and the speed and acceleration of the switched reluctance motor during operation;

[0085] S7, controlling the switched reluctance motor according to the obtained optimal control parameters, and realizing full-process high-performance speed regulation control of the switched reluctance motor under the variable speed and variable acceleration mode.

[0086] In order to illustrate the effect of the high-performance speed regulation control parameter optimization method of the switched reluctance motor based on the super-helical algorithm provided by the present invention, a 12 / 8-pole switched reluctance motor is taken as an example below, and its main technical parameters are shown in Table 1 to verify the effect of the control parameter optimization method provided by the present invention.

[0087]

[0088] According to the technical parameters shown in Table 1, within the rated speed range of the motor, if any 100 r / min As the starting point, and any 60 r / min20 groups of speed values ​​are selected at equal intervals; at the same time, according to the operating conditions of the switched reluctance motor, if its maximum angular acceleration is 80 rad / s 2 , and within the maximum angular acceleration range, if any 10 rad / s 2 As the starting point, and any 3 rad / s 2 20 groups of angular acceleration data are selected in sequence at equal intervals. For each group of rotation speed and acceleration data selected, the control parameter optimization method provided by the present invention is used to obtain the corresponding optimal control parameters, as shown in Table 2.

[0089]

[0090] According to the optimal control parameters and their corresponding motor speed and acceleration data shown in Table 2, the functional relationship between the optimal parameters and the motor speed and acceleration is obtained by numerical fitting method, which is as follows:

[0091]

[0092] In the formula ω is the actual speed of the switched reluctance motor, and ω′ is the first-order derivative of the actual speed of the switched reluctance motor, i.e., its angular acceleration.

[0093] At the same time, in order to further illustrate the effect of the control parameter optimization method provided by the present invention, the control parameter optimization method provided by the present invention (hereinafter referred to as the present method) is compared and analyzed with the traditional fixed control parameter control method (hereinafter referred to as the traditional method), and the optimal control parameters obtained by using the improved walking optimization algorithm at the rated speed and maximum acceleration of the motor are taken as the control parameters of the traditional method. The optimal control parameters are shown in Table 3.

[0094]

[0095] Take any two sets of data within the range of the motor's rated speed and maximum angular acceleration. For example, the motor speed and acceleration are 300 and 400 respectively. r / min , 60 rad / s 2 and 1000 r / min , 70 rad / s 2 For these two sets of speed and acceleration data, the switch reluctance motor speed control system is controlled by this method and the traditional method, and the torque ripple coefficient and steady-state control accuracy are shown in Table 4.

[0096]

[0097] It can be seen from Table 4 that compared with the traditional method, the motor torque ripple coefficient obtained by the present method decreased by 19.6% and 26.04% respectively, while the steady-state control accuracy increased by 1.33% and 1.16% respectively. This further proves that the control effect of the switched reluctance motor speed control system is effectively improved by the control parameter optimization method provided by the present invention.

Claims

1. A method for optimizing high-performance speed control parameters of a switched reluctance motor based on a super-helical algorithm, characterized in that: The following steps are involved: S1, taking the super-helical control parameters as the optimization object, taking the torque ripple coefficient of the switched reluctance motor and its steady-state control accuracy as the optimization target, constructing a mathematical model between the optimization object and the optimization target; the optimization object includes the sliding surface coefficient c, the power term approach rate gain α and the hyperbolic tangent function curvature gain δ of the super-helical control algorithm; the optimization target is the torque ripple coefficient and steady-state control accuracy of the switched reluctance motor; its mathematical model is: （5）； In the above formula, ε is the torque ripple coefficient of the switched reluctance motor. is the steady-state control accuracy of the switched reluctance motor, T max and T min Respectively represent the maximum torque and minimum torque of the switched reluctance motor, T ave is the average torque of the switched reluctance motor; is the given speed of the switched reluctance motor, Δω i is the deviation between the actual speed of the switched reluctance motor collected for the i-th time and its given speed; S2, constructing its multi-objective optimization fitness function according to the mathematical model between the obtained optimization object and the optimization target; S3, determining the speed and acceleration range of the switched reluctance motor according to its operating conditions, and randomly selecting n groups of speed and acceleration data at equal intervals within the above range; S4, for each set of selected speed and acceleration data, an improved walking optimization algorithm is used to obtain the optimal value of each control parameter of the superhelical control; S5, according to the obtained n groups of optimal control parameters and their corresponding motor speed and acceleration data, a numerical fitting method is used to obtain the functional relationship between each optimal control parameter and its speed and acceleration; S6, obtaining the optimal control parameters under the speed and acceleration according to the obtained functional relationship of the optimal control parameters and the speed and acceleration of the switched reluctance motor during operation; S7, controlling the switched reluctance motor according to the obtained optimal control parameters, and realizing full-process high-performance speed regulation control of the switched reluctance motor under the variable speed and variable acceleration mode.

2. The method for optimizing the high-performance speed control parameters of a switched reluctance motor based on a super-helical algorithm according to claim 1 is characterized in that: The step S2 is specifically as follows: According to formula (5), the multi-objective optimization fitness function F is constructed as follows: （6）； In the formula, k1 and k2 are weight coefficients, and k1+k2 =1.

3. The method for optimizing high-performance speed control parameters of a switched reluctance motor based on a super-helical algorithm according to claim 1 is characterized in that: The step S4 adopts an improved walking optimization algorithm to obtain the optimal values ​​of each control parameter of the superhelical control, specifically: S 41 , set relevant parameters: including the maximum number of iterations M, the number of hikers H, the hiking range L and the individual coordinates of the first generation of hikers; among them, the individual coordinates of the first generation of hikers are randomly selected within the hiking range, specifically: （7）； Where (x c1,0 , y c1,0 ), (x α1,0 , y α1,0 ), (x δ1,0 , y δ1,0 ) are the individual coordinates of the first generation of hikers, random() represents a random number between 0 and 1; The individual coordinates of the i-th first-generation hiker are shown in formula (8), i=2,3...H; （8）； Where (x ci,0 , y ci,0 ), (x αi,0 , y αi,0 ), (x δi,0 , y δi,0 ) are the individual coordinates of the i-th first-generation hiker, (x ci-1,0 , y ci-1,0 ), (x αi-1,0 , y αi-1,0 ), (x δi-1,0 , y δi-1,0 ) are the individual coordinates of the i-1th first-generation hiker, μ1 and μ2 are random values ​​in the range of (0,1); S 42 , calculate and compare the fitness values ​​corresponding to each individual of the first generation of hikers according to formula (6), retain the minimum fitness value and its corresponding individual coordinates of the hiker, and mark the retained individual coordinates of the hiker as the leader coordinates (X c ,Y c )、(X α ,Y α ) and (X δ ,Y δ ); S 43 , according to the leader's coordinates, update the next generation of hikers' individual coordinates; among them, the j-th hiker's individual coordinates are: （9）； In the above formula (θ cj,x , θ cj,y ), (θ αj,x , θ αj,y ), (θ δj,x , θ δj,y ) are all random angles in [0, 50°] (j=1,2,3…H), m is the current generation, M is the maximum number of iterations, γ1 and γ2 are random numbers in (0,1), and S is an integer in [1,3]; S 44 , according to step S 43 The individual coordinates of the next generation of hikers are obtained, and the fitness values ​​corresponding to each individual of the next generation of hikers are calculated and compared using formula (6); S 45 , comparison step S 44 Is the obtained minimum fitness value of the individual hiker less than the reserved minimum fitness value? If so, update the coordinates of the leader corresponding to the reserved minimum fitness value; otherwise, execute step S 46 ; S 46 , determine whether the number of iterations reaches the maximum number of iterations M. If not, add 1 to the number of iterations and return to step S 43 ; Otherwise, output the leader's coordinates and calculate the optimal values ​​of the optimization objects c, α, and δ, as follows: （10）； In the above formula, X c and Y c Respectively represent the horizontal and vertical coordinates of the optimization object c, X α and Y α Respectively represent the horizontal and vertical coordinates of the optimization object α, X δ and Y δ They represent the horizontal and vertical coordinates of the optimization object δ respectively.

4. The method for optimizing high-performance speed control parameters of a switched reluctance motor based on a super-helical algorithm according to claim 3 is characterized in that: The step S5 uses a numerical fitting method to obtain the functional relationship between each optimal control parameter and its rotation speed and acceleration, which is as follows: (11)； In the above formula, ω is the actual speed of the switched reluctance motor, ω′ is the angular acceleration of the switched reluctance motor, and A0~A6, B0~B6, and C0~C6 are all constant coefficients.

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