Switched reluctance motor angle position control system and optimization calibration method

Abstract

The invention provides a switched reluctance motor angle position control system and an optimization calibration method. The method comprises the following steps: initializing system parameters of a switched reluctance motor; optimizing and calibrating a motor operation angle based on an improved Kepler optimization algorithm; and the motor performs real-time fine-tuning operation. An improved Kepler optimization algorithm is introduced, rapid global search is carried out, an angle calibration point candidate range is rapidly positioned in the initial stage of motor operation, and the time cost of manual intervention or blind calculation is reduced. Fusing a perturbation and observation algorithm, observing feedback data in a motor operation state, finely adjusting candidate calibration points, improving calibration precision, and ensuring adaptation to dynamic working condition changes; the sensor data is collected in real time, the controller and the upper computer work cooperatively, dynamic adjustment and optimization of calibration points are achieved, and therefore the synchronism and operation efficiency of the system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of switched reluctance motor, and particularly relates to a switched reluctance motor angle position control system and an optimization calibration method. BACKGROUND

[0002] With the rapid development of global industrial field, switched reluctance motor as an important energy element plays an important role in mechanical and industrial systems. In recent years, it has shown significant advantages in motor drive, energy conversion and other fields, and is widely used in electric vehicles, energy storage systems, robots and other specific applications. In the design of the angle position control system of the switched reluctance motor, the angle calibration point is one of the key parameters. Good angle calibration point can ensure efficient operation, accurate synchronization and long service life of the motor. However, the current method usually relies on manual measurement feedback through external sensors or calculation based on analog algorithm, which has certain hidden labor cost increase and uncertainty of control results, which affects whether the control performance is optimal.

[0003] Therefore, it is necessary to improve the existing switched reluctance motor angle position control method. In order to solve these problems, the present application proposes an angle calibration method for switched reluctance motor control. By introducing improved Kepler optimization and disturbance observation fusion algorithm, the controller and host computer design can automatically adjust the angle calibration point to improve the accuracy, efficiency and robustness of the system. The algorithm can use the fast search ability of the optimization algorithm to adjust in real time combined with sensor data to ensure the accuracy of the angle calibration point. SUMMARY

[0004] The technical problem to be solved by the present application is that the angle calibration point in the switched reluctance motor angle position control system in the prior art relies on manual measurement or analog algorithm calculation, resulting in hidden cost increase, control result uncertainty increase and control performance difficult to achieve optimal problem. The present application proposes an optimization calibration method for switched reluctance motor angle position control based on improved Kepler optimization (IKOA) and optimized variable-step perturb and observe (OIP&O) fusion algorithm.

[0005] The overall technical concept is: introducing an improved Kepler optimization algorithm, using the fast global search capability of the algorithm to quickly locate the candidate range of angle calibration points in the initial stage of motor operation, reducing the time cost of manual intervention or blind calculation in the traditional method. Fusion disturbance observation algorithm, through real-time disturbance and observation of feedback data under motor running state, fine-tune the candidate calibration point, further improve the calibration accuracy, ensure its adaptability to dynamic working condition change; Based on the real-time acquisition of sensor data, cooperate with the controller and the host computer, realize the automatic adjustment and optimization of the calibration point, so as to improve the synchronization and operation efficiency of the system. The fusion algorithm is deployed on the host computer, and the accuracy of the calibration point is verified through simulation and actual operation, ensuring the robustness and long-life operation of the system under different loads and environments.

[0006] The technical solution adopted by the present application to solve its technical problems is: a switched reluctance motor angle position control system, comprising a DSP controller, a host computer, a loading motor, a loading motor controller, a switched reluctance motor, a Hall position sensor, a current sensor, a rectifier and a power converter, wherein,

[0007] The host computer is connected to the loading motor through the loading motor controller, and the host computer sends control signals to the loading motor controller, and the loading motor controller controls the operation of the loading motor;

[0008] The Hall position sensor is connected with the switched reluctance motor and the DSP controller, and is used for detecting the position and speed of the switched reluctance motor and sending high and low level signals to the DSP controller, and the DSP controller collects and processes the position and speed signals; wherein, the processing of the position signal: through pulse counting or encoder analysis, the current angle of the rotor is determined, which is used for controlling the energization time of the motor phase winding (i.e. phase control); the processing of the speed signal: the speed is calculated according to the time interval of the position signal, which is used as the input of the speed feedback closed loop control; fault detection: if the position signal is abnormal, such as missing pulse, the protection mechanism is started, such as stopping or reducing speed.

[0009] The current sensor is connected with the switched reluctance motor and the DSP controller, and is used for detecting the current of the switched reluctance motor phase, such as the current of A phase, B phase and C phase, and sending the current signal to the DSP controller for processing; the current sensor collects the current signal of each phase, which is converted into digital quantity by the ADC module, and then the current signal is read by the DSP controller at regular intervals, which is used for the control and protection of the motor.

[0010] The rectifier, power converter, switched reluctance motor and loading motor are connected through high-voltage strong electric circuit and elastic coupling to form an electric energy transmission and load simulation loop; the DSP controller is connected with the power converter, and the DSP controller controls the switching device of the power converter through PWM signal to realize speed regulation, torque control and efficiency optimization. Among them, the precise speed regulation is realized by adjusting the PWM duty cycle to change the motor phase voltage to control the speed; the torque control is realized by adjusting the energization timing according to the current feedback to optimize the torque output; and the efficiency optimization is realized by dynamically adjusting the switching frequency and phase to reduce the switching loss.

[0011] The DSP controller is connected with the upper computer, and is used for realizing advanced control debugging, monitoring and data interaction of the DSP controller through the upper computer.

[0012] An optimization and setting method of angle position control of a switched reluctance motor, applied to the angle position control system of the switched reluctance motor, further comprising the following steps:

[0013] S1: system parameter initialization of the switched reluctance motor;

[0014] S2: motor running angle optimization and setting based on the improved Kepler optimization algorithm;

[0015] S3: real-time fine adjustment of the motor.

[0016] Further, step S1: system parameter initialization of the switched reluctance motor, specifically comprising

[0017] Through the upper computer interaction setting and transmission to the DSP controller, the rated parameters of the switched reluctance motor are input, including motor rated phase current I N , maximum instantaneous motor phase current i m , rated speed N R , rated torque T N and rated output power P N ;

[0018] Through the external sensor measurement and transmission to the upper computer, the real-time data of the switched reluctance motor are input, including switched reluctance motor running input power P in , switched reluctance motor running efficiency η RPM , switched reluctance motor input three-phase instantaneous current i RPM , real-time speed n RPM and the corresponding setting lead angle θ of the speed, wherein the lead angle θ is the lead angle in the angle position control algorithm when running to the rated speed.

[0019] The loading power of the loading motor is controlled by the upper computer and is initially P load , and the initial loading power P load is equal to the rated output power P N.

[0020] Furthermore, step S2: optimizing and calibrating the motor operating angle based on the improved Kepler optimization algorithm includes initializing and setting parameters of the improved Kepler optimization algorithm and optimizing and calibrating the motor operating angle, specifically including the following steps:

[0021] S2.1: IKOA parameter initialization settings

[0022] Initialize the positions of N planets in the IKOA algorithm search space, that is, N initial switched reluctance motor advance angle values, X = {θ1, θ2, ..., θ N}, the initialization formula is as follows:

[0023] X i =X i,lb +r0×(X i,ub -X i,lb ) (1)

[0024] Where, X i,ub With X i,lb are the upper and lower bounds of the search space of the advance angle respectively; r0 is a random number between 0 and 1, i∈(1~N), 1, 2, 3…N is the sequence number of the advance angle θ;

[0025] Initialize orbital parameters e and orbital period T i , initialized before the optimization begins, orbital eccentricity e and orbital period T i The initialization formula is as follows:

[0026] e i =r1 (2)

[0027] T i =|r n | (3)

[0028] Where r1 is a random number between 0 and 1; r n is a random number based on normal distribution; i∈(1~N);

[0029] The advance angle is the core parameter that affects the performance of the switched reluctance motor. The advance angle value of the switched reluctance control is set by the host computer, the advance angle value initialized by the switched reluctance controller is transmitted, and the output power n of the switched reluctance motor is sampled at each advance angle in turn. RPM and operating efficiency η RPM , based on n RPM and η RPM Calculate the fitness value J to determine the optimal advance angle position X S (t), as shown below:

[0030] J=α RPM ·ηRPM +(1-α RPM )·n RPM (4)

[0031] Where, α RPM The control factor of the timing performance target is used to advance the angle. By adjusting the factor, the operating target of the switched reluctance motor can be adjusted to operating efficiency or speed at a fixed output power.

[0032] S2.2: Calculate IKOA advance angle force

[0033] Calculate the advance angle relative to the sun's gravitational force F g , the sun is the optimal advance angle in the angle position control of the switched reluctance motor, and the gravitational force is as follows:

[0034]

[0035] Where t represents the current iteration number; μ(t) is a function that decreases exponentially with the iteration number t, and is used for the search accuracy of angle optimization control, and is defined as shown in Equation (12); and Represent the optimal advance angle X s and advance angle X i The quality of M s and m s The normalized value of is calculated and normalized according to formula (8) and (9); r2 is a random number between 0 and 1; is the optimal advance angle X s and advance angle X i The Euclidean distance of R is obtained from formula (7); i Expressed as Euclidean normalized distance; ε is a small value to avoid division by zero. Optimal advance angle X s and advance angle X i The Euclidean distance and related parameters can be calculated by formulas (6) to (12), as shown below:

[0036]

[0037]

[0038] Where r3 is a random number in the range of 0 to 1; M S Expressed as the optimal advance angle X s Mass; m i Indicated as X i The mass of γ is a fixed value; μ0 is an initial value; t and T max Represent the current number of iterations and the maximum number of iterations respectively;

[0039] S2.3: Calculate the lead angle orbital velocity in IKOA

[0040] The lead angle velocity is calculated by the distance from the optimal lead angle, and the lead angle velocity rises as the distance from the optimal lead angle is closer, and falls as the distance from the optimal lead angle is farther. The lead angle velocity is calculated by equations (13)-(17) as follows:

[0041]

[0042] M = (r4 x (1-r5) + r5) (16)

[0043]

[0044]

[0045] In the formula, V i (t) represents the current lead angle velocity; r4, r5, r6 and r7 are random numbers in the range of 0 to 1; X a and X b are two random solutions in the current solution; a i (t) is the semi-major axis of the i-th lead angle elliptical orbit, which can be calculated by equation (21); a t is the lead angle velocity update step, which can be calculated by equation (22); R i-norm (t) represents the normalized Euclidean distance between X s and X i , which can be calculated by equation (23), and each calculation formula is as follows:

[0046]

[0047] In the formula, a max and a min are the upper limit and lower limit of the step size, respectively.

[0048]

[0049] The purpose of equation (23) is to calculate the percentage of steps in which each lead angle changes; if R i-norm (t)≤0.5, the lead angle is close to the optimal lead angle, and the speed will be increased to prevent drifting to the optimal lead angle due to the huge gravity of the optimal lead angle. Otherwise, the lead angle will slow down;

[0050] S2.4: Local optimization of escape IKOA

[0051] To improve the performance of the improved IKOA algorithm, the adaptive disturbance switching probability is optimized to enhance the ability of the algorithm to jump out of the local optimal solution. A dynamically adjusted probability P r-normModel. The model uses a larger threshold at the beginning of the algorithm to expand the search range and improve global search ability; while in the later stage of the algorithm, the threshold is reduced to narrow the search range, thereby improving search accuracy and convergence speed. Through adaptive adjustment, the model can effectively balance between global search and local refinement, improving the overall performance of the algorithm, and adaptive P r-norm as shown in formula (24):

[0052] P r-norm = 0.7-0.4(T max -t) / T max (24)

[0053] In the formula, t is the iteration number of the probability model at the current time; T max is the maximum iteration number of the probability model running;

[0054] S2.5: Update the crank angle position in IKOA

[0055] By simulating the gravity of the optimal crank angle on the crank angle, by regularly switching the search direction, breaking through the local optimal region, providing the crank angle with better opportunities to explore the entire space. In IKOA, the exploration operation is simulated when the crank angle is far away from the optimal crank angle, and the mining operation is realized when the crank angle is close to the optimal crank angle. The update of the position is as follows:

[0056]

[0057] In the formula, r8 is a random number between 0 and 1.

[0058] By constantly updating the position of the crank angle, the distance between the optimal crank angle and the crank angle is simulated to change naturally over time. When the crank angle is getting closer and closer to the optimal crank angle, the mining operator is activated to improve the convergence speed; while when the optimal crank angle is getting farther and farther away from the crank angle, the exploration operator is activated to minimize the local optimal state, as shown in the following formula:

[0059]

[0060] In the formula, h is an adaptive factor that controls the distance between the optimal crank angle and the current crank angle at time t, calculated as shown in the following formula:

[0061]

[0062] In the formula, r9 is a normally distributed random number; η is a linearly decreasing factor from 1 to -2, calculated as shown in the following formula:

[0063] η = (a2-1) x r 10 +1 (28)

[0064] In the formula, r10 is a random number between 0 and 1; a2 is a control parameter of η, calculated as follows:

[0065]

[0066] wherein, is a cycle number control parameter; % is a remainder operation.

[0067] When the iteration number of the above IKOA algorithm reaches a set value and the current optimal advance angle is tracked, in order to further improve the accuracy of the algorithm to the optimal advance angle, an optimized variable step size perturbation algorithm is used for small range local search to find the global optimal advance angle.

[0068] Further, step S3: real-time fine adjustment of the motor, specifically including

[0069] The local optimal advance angle obtained in step S2 is input into an optimized variable step size perturbation observation algorithm (Improved Perturb and Observe, OIP&O), a small angle perturbation (such as ±0.1°) is applied in real time by the controller, and the speed stability and operating efficiency of the motor running state are monitored by the sensor. According to the feedback data, it is judged whether the perturbation direction optimizes the performance, and gradually iterated to the optimal calibration point. Finally, the roughness of the initial search stage is overcome, the accuracy of the calibration point is further improved, and the dynamic changes of the load mutation and temperature drift in the motor running are adapted, to ensure that the calibration point is always close to the actual optimal state. The specific steps are as follows:

[0070] By real-time sampling and calculation of the output speed and efficiency of the switched reluctance motor system and their change rates, the perturbation step size is dynamically adjusted, thereby improving the corresponding speed of the angle calibration. The perturbation process is shown in the following formula:

[0071]

[0072] wherein, n SRM is the measured output speed of the switched reluctance motor system; n SRM (t) and n SRM (t-1) are the speed values before and after the tth perturbation; Δn SRM is the speed increment caused by the duty cycle change from the (t-1)th iteration to the tth iteration; Δη RPM (t) is the efficiency increment caused by the duty cycle change from the (t-1)th iteration to the tth iteration; Δθ(t) is the advance angle change step size at the tth iteration; Δθ step is the fixed change amount of the advance angle. If Δη SRM is positive, continue to perturb in the current direction; if Δη SRM is negative, perturb in the opposite direction.

[0073] The optimal advance angle optimized by steps S2 and S3 is transmitted to the switched reluctance motor controller through the host computer, the controller generates corresponding switching signals according to the calibrated advance angle and in combination with current switched reluctance motor operation parameters, and drives the power conversion unit to adjust the energization timing of each phase winding. At the same time, the external sensor system continuously collects the efficiency and speed data of the switched reluctance motor, and feeds back to the host computer IKOA-OIP&O advance angle calibration algorithm, forming a closed loop control, and real-time fine adjustment and correction of the deviation of the calibration point.

[0074] The present application has the advantages that: the present application provides a switched reluctance motor angle position control optimization calibration method, which automatically adjusts the angle calibration point of the switched reluctance motor through the improved Kepler optimization and disturbance observation fusion algorithm, significantly improves the control accuracy, operation efficiency and robustness of the system, reduces the implicit labor cost and uncertainty, and thus promotes the efficient application and long-term stable operation of the switched reluctance motor in the fields of electric vehicles, energy storage systems and robots. BRIEF DESCRIPTION OF DRAWINGS

[0075] The present application will be further described below in conjunction with the drawings and examples.

[0076] Figure 1 is a schematic block diagram of the hardware structure of the switched reluctance motor angle position control system of the present application.

[0077] Figure 2 is a control schematic diagram of the switched reluctance motor calibration system of the present application.

[0078] Figure 3 is a flowchart of the online control and advance angle updating of the switched reluctance motor of the present application.

[0079] Figure 4 is the IKOA-OIP&O advance angle optimization control flowchart of the switched reluctance motor of the present application. DETAILED DESCRIPTION

[0080] The present application will now be described in detail in conjunction with the drawings. This figure is a simplified schematic diagram, which only schematically illustrates the basic structure of the present application, and therefore only shows the components related to the present application.

[0081] As shown in Figure 1 and Figure 2 , a switched reluctance motor angle position control system includes a DSP controller, a host computer, a loading motor, a loading motor controller, a switched reluctance motor, a Hall position sensor, a current sensor, a rectifier and a power converter, wherein,

[0082] The host computer is connected to the loading motor through the loading motor controller, and the host computer sends control signals to the loading motor controller, and the loading motor controller controls the operation of the loading motor;

[0083] Hall position sensor, connected with the switched reluctance motor and the DSP controller, is used for detecting the position and rotating speed of the switched reluctance motor and sending the electric signals of the position and rotating speed signals to the DSP controller, and the DSP controller samples and processes the position and rotating speed signals;The processing of the position signal: the current angle of the rotor is determined through pulse counting or encoder analysis, which is used for controlling the energizing time of the motor phase winding (i.e. phase control);The processing of the rotating speed signal: the rotating speed is calculated according to the time interval of the position signal, which is used as the input of the speed feedback closed-loop control;Fault detection: if the position signal is abnormal, such as missing pulse, the protection mechanism is started, such as stopping or reducing the speed.

[0084] Current sensor, connected with the switched reluctance motor and the DSP controller, is used for detecting the current of the switched reluctance motor phase, such as the current of A phase, B phase and C phase, and sending the current signal to the DSP controller for processing;The current signal of each phase is collected by the current sensor, converted into digital quantity by the ADC module, and then read by the DSP controller at a fixed time, which is used for the control and protection of the motor.

[0085] The rectifier, power converter, switched reluctance motor and loaded motor are connected through high-voltage strong electric lines and elastic couplings to form an electric energy transmission and load simulation loop;The DSP controller is connected with the power converter, and the DSP controller controls the switching devices of the power converter through PWM signals to realize speed regulation, torque control and efficiency optimization;The accurate speed regulation is realized by adjusting the PWM duty cycle to change the motor phase voltage and control the rotating speed;The torque control is realized by adjusting the energizing time sequence according to the current feedback to optimize the torque output;The efficiency optimization is realized by dynamically adjusting the switching frequency and phase to reduce the switching loss.

[0086] The DSP controller is connected with the upper computer, which is used for realizing advanced control debugging, monitoring and data interaction of the DSP controller through the upper computer.

[0087] The optimization and calibration method of the angle position control of the switched reluctance motor is applied to the angle position control system of the switched reluctance motor, and further comprises the following steps:

[0088] S1: the system parameters of the switched reluctance motor are initialized;

[0089] S2: the motor running angle optimization and calibration based on the improved Kepler optimization algorithm;

[0090] S3: the real-time fine adjustment of the motor.

[0091] The optimization and calibration method will be described in detail below with reference to the accompanying drawings. Figure 3 and Figure 4 The optimization and calibration method will be described in detail below with reference to the accompanying drawings.

[0092] Step S1: Initialization of switched reluctance motor system parameters, including

[0093] The rated parameters of the switched reluctance motor are input through the interactive setting of the host computer and transmitted to the DSP controller: the rated phase current I N , the instantaneous maximum value of the motor phase current i m , Rated speed N R , rated torque T N and rated output power P N ;

[0094] Measured by external sensors and transmitted to the host computer, the real-time data of the switched reluctance motor operation is input: the input power P of the switched reluctance motor operation in , the operating efficiency η of the switched reluctance motor RPM , the input three-phase current instantaneous current i of the switched reluctance motor RPM , real-time speed n RPM and the corresponding set advance angle θ of the speed;

[0095] The loading power of the loading motor is controlled by the host computer and is initially P load , and the initial loading power P load Equal to the rated output power P of the switched reluctance motor N .

[0096] Step S2: Optimal calibration of the motor operating angle based on the improved Kepler optimization algorithm includes initialization setting of parameters of the improved Kepler optimization algorithm and optimal calibration of the motor operating angle.

[0097] Among them, the parameter initialization settings of the Kepler optimization algorithm are improved, including

[0098] S2.1: IKOA parameter initialization settings

[0099] Initialize the positions of N planets in the IKOA algorithm search space, that is, N initial switched reluctance motor advance angle values, X = {θ1, θ2, ..., θ N}, the initialization formula is as follows:

[0100] X i =X i,lb +r0×(X i,ub -X i,lb ) (1)

[0101] Where, X i,ub With X i,lb are the upper and lower bounds of the search space respectively; r0 is a random number between 0 and 1, i∈(1~N);

[0102] Initialize orbital parameters e and orbital period T i, initialized before the optimization begins, the orbital eccentricity and orbital period initialization formulas are as follows:

[0103] e i =r1 (2)

[0104] T i =|r n | (3)

[0105] Where r1 is a random number between 0 and 1; r n is a random number based on normal distribution; i∈(1~N);

[0106] The advance angle is the core parameter that affects the performance of the switched reluctance motor. The advance angle value of the switched reluctance control is set by the host computer, the advance angle value initialized by the switched reluctance controller is transmitted, and the output power n of the switched reluctance motor is sampled at each advance angle in turn. RPM and operating efficiency η RPM , based on n RPM and η RPM Calculate the fitness value J to determine the optimal advance angle position X S (t), as shown below:

[0107] J=α RPM ·η RPM +(1-α RPM )·n RPM (4)

[0108] Where, α RPM The control factor of the timing performance target is used to advance the angle. By adjusting the factor, the operating target of the switched reluctance motor can be adjusted to operating efficiency or speed at a fixed output power.

[0109] S2.2: Calculate IKOA advance angle force

[0110] Calculate the advance angle relative to the sun's gravitational force F g , the sun is the optimal advance angle in the angle position control of the switched reluctance motor, and the gravitational force is as follows:

[0111]

[0112] Where t represents the current iteration number; μ(t) is a function that decreases exponentially with the iteration number t, which is used to control the search accuracy and is defined as shown in formula (12); and Represents Sun X s and advance angle X i The quality of M s and m snormalized value of the distance between the sun X is the Euclidean distance between the sun X s and the advanced angle X i , which can be calculated by equation (7); R i is the Euclidean normalized distance; ε is a small value to avoid division by zero. The sun X s and the advanced angle X i The Euclidean distance and related parameters can be calculated by equations (6)-(12) as follows:

[0113]

[0114] where r3 is a random number in the range of 0-1; M S is the mass of the sun X s ; m i is the mass of X i ; γ is a fixed value; μ0 is an initial value; T max is the current iteration number.

[0115] S2.3: Calculate the advanced angle orbital velocity in IKOA

[0116] Calculate the advanced angle velocity, which is calculated by the distance from the sun. The advanced angle velocity increases as the distance from the sun decreases and decreases as the distance from the sun increases. The advanced angle velocity is calculated by equations (13)-(17) as follows:

[0117]

[0118] l = U x M x L (14)

[0119]

[0120] M = (r4 x (1-r5) + r5) (16)

[0121]

[0122] where V i (t) is the current advanced angle velocity; r4, r5, r6 and r7 are random numbers in the range of 0-1; X a and X b are two random solutions in the current solution; a i (t) is the semi-major axis of the i-th advanced angle elliptical orbit, which can be calculated by equation (21); α t is the advanced angle velocity update step, which can be calculated by equation (22); R i-norm (t) is the Euclidean distance between X s and Xi The normalization of the Euclidean distance between them can be calculated by equation (23), and each calculation formula is as follows:

[0123]

[0124] In the formula, α max and α min are the upper limit value and the lower limit value of the step size, respectively.

[0125]

[0126] The purpose of equation (23) is to calculate the percentage of steps in which each lead angle changes; if R i-norm (t)≤0.5, the lead angle is close to the sun, and the speed will be increased to prevent drifting towards the sun due to the huge gravitational force of the sun. Otherwise, the lead angle will slow down;

[0127] The motor running angle optimization calibration specifically includes:

[0128] S2.4: escape IKOA local optimization

[0129] In order to improve the performance of the improved IKOA algorithm, the adaptive disturbance switching probability is optimized, aiming to enhance the ability of the algorithm to jump out of the local optimal solution. A dynamically adjusted probability P r-norm is proposed. This model uses a larger threshold at the beginning of the algorithm to expand the search range and improve the global search ability; while in the later stage of the algorithm, the threshold is reduced to narrow the search range, thereby improving the search precision and convergence speed. Through adaptive adjustment, this model can effectively balance between global search and local refinement, improving the overall performance of the algorithm. The adaptive P r-norm is shown in equation (24):

[0130] P r-norm = 0.7-0.4(T max -t) / T max (24)

[0131] In the formula, t is the number of iterations at the current time of the probability model; T max is the maximum number of iterations of the probability model;

[0132] S2.5: update the lead angle position in IKOA

[0133] By simulating the gravity of the sun on the lead angle, by regularly switching the search direction, breaking through the local optimal area, providing a better opportunity for the lead angle to explore the entire space. In IKOA, simulate the exploration operation when the lead angle is far from the sun, and realize the mining operation when the lead angle is close to the sun. The update of the position is as follows:

[0134]

[0135] wherein r8 is a random number between 0 and 1.

[0136] The position of the advanced angle is constantly updated to simulate the natural change of the distance between the sun and the advanced angle over time. When the advanced angle is getting closer to the sun, the exploitation operator is activated to improve the convergence speed; while when the sun is getting farther away from the advanced angle, the exploration operator is activated to minimize the state of falling into a local optimum as shown in the following formula:

[0137]

[0138] wherein h is an adaptive factor of the distance between the sun and the current advanced angle at the control time t, calculated as shown in the following formula:

[0139]

[0140] wherein r9 is a randomly generated number in a normal distribution; η is a linearly decreasing factor from 1 to -2, calculated as shown in the following formula:

[0141] η = (a2-1) x r 10 +1 (28)

[0142] wherein r 10 is a random number between 0 and 1; a2 is a control parameter of η, calculated as shown in the following formula:

[0143]

[0144] wherein is a control parameter of the number of cycles; % is a remainder operation.

[0145] When the number of iterations of the above IKOA algorithm reaches a set value and the current optimal advanced angle is tracked, in order to further improve the accuracy of the algorithm to the optimal advanced angle, an optimized variable step size perturbation algorithm is used for small range local search to find the global optimal advanced angle.

[0146] Further, step S3: real-time fine adjustment of the motor operation, specifically including

[0147] The partial optimal advance angle obtained in step S2 is input into an improved perturb and observe (OIP&O) optimization type variable step size disturbance observation algorithm, a small angle disturbance (such as ±0.1°) is applied in real time by a controller, and the speed stability and operating efficiency of the motor are monitored by a sensor. Whether the disturbance direction optimizes the performance is judged according to the feedback data, and the optimal calibration point is gradually adjusted. Finally, the accuracy of the calibration point is further improved, and the dynamic changes of the load mutation and temperature drift in the motor operation are adapted to ensure that the calibrated advance angle is always close to the actual optimal state. The specific steps are as follows:

[0148] The output speed and efficiency of the switched reluctance motor system and their change rates are sampled and calculated in real time, and the disturbance step size is dynamically adjusted, so that the corresponding speed of the angle calibration is improved. The disturbance process is shown in the following formula:

[0149]

[0150] In the formula, n SRM is the measured output speed of the switched reluctance motor system; n SRM (t) and n SRM (t-1) are the speed values before and after the tth disturbance; Δn SRM is the speed increment caused by the duty cycle change from the t-1th iteration to the tth iteration; Δη RPM (t) is the efficiency increment caused by the duty cycle change from the t-1th iteration to the tth iteration; Δθ(t) is the advance angle change step size at the tth iteration; Δθ step is the fixed change of the advance angle. If Δη SRM is positive, continue to disturb in the current direction; if Δη SRM is negative, disturb in the opposite direction.

[0151] The optimal advance angle optimized in steps S2 and S3 is transmitted to the switched reluctance motor controller through the upper computer. The controller generates corresponding switching signals according to the calibrated advance angle and the current switched reluctance motor operating parameters, and drives the power conversion unit to adjust the energization sequence of each phase winding. At the same time, the external sensor system continuously collects the efficiency and speed data of the switched reluctance motor, and feeds back to the upper computer IKOA-OIP&O advance angle calibration algorithm, forming a closed loop control, and real-time fine adjustment and correction of the deviation of the calibration point.

[0152] Based on the above ideal embodiments according to the present application, through the above description, relevant personnel can make various changes and modifications without deviating from the scope of the present application. The technical scope of the present application is not limited to the contents of the specification, and must be determined by the scope of the claims.

Claims

1. A switched reluctance motor angle position control system, characterized by: It includes DSP controller, host computer, loading motor, loading motor controller, switched reluctance motor, Hall position sensor, current sensor, rectifier and power converter, among which, The host computer is connected to the loading motor through the loading motor controller, and a control signal is sent to the loading motor controller through the host computer, and the operation of the loading motor is controlled by the loading motor controller; The Hall position sensor is connected to the switched reluctance motor and the DSP controller to detect the position and speed of the switched reluctance motor and send the position and speed signals to the DSP controller, which processes the position and speed signals. A current sensor is connected to the switched reluctance motor and the DSP controller, and is used to detect the phase current of the switched reluctance motor and send the current signal to the DSP controller for processing; The rectifier, power converter, switched reluctance motor, and loading motor are connected through high-voltage power lines and elastic couplings to form a power transmission and load simulation loop. The DSP controller is connected to the power converter and controls the switching devices of the power converter through PWM signals to achieve speed regulation, torque control, and efficiency optimization. The DSP controller is connected to the host computer to implement advanced control debugging, monitoring and data interaction of the DSP controller through the host computer.

2. A method for optimizing and calibrating the angular position control of a switched reluctance motor, characterized by: The switched reluctance motor angle position control system according to claim 1 further comprises the following steps: S1: Switched reluctance motor system parameter initialization; S2: Motor operating angle optimization calibration based on improved Kepler optimization algorithm; S3: The motor performs real-time fine-tuning operation.

3. The method for optimizing and calibrating the angle position control of a switched reluctance motor according to claim 2, wherein: Step S1: Initialization of switched reluctance motor system parameters, including Enter the rated parameters of the switched reluctance motor: motor rated phase current I N , the instantaneous maximum value of the motor phase current i m , Rated speed N R , rated torque T N and rated output power P N ; Input the real-time data of the switched reluctance motor operation: the switched reluctance motor operation input power P in , the operating efficiency η of the switched reluctance motor RPM , the input three-phase current instantaneous current i of the switched reluctance motor RPM , real-time speed n RPM and the corresponding set advance angle θ of the speed; The loading power of the loading motor is controlled by the host computer and is initially P load , and the initial loading power P load Equal to the rated output power P of the switched reluctance motor N .

4. The method for optimizing and calibrating the angle position control of a switched reluctance motor according to claim 3, wherein: Step S2: Optimal calibration of the motor operating angle based on the improved Kepler optimization algorithm includes initializing the parameters of the improved Kepler optimization algorithm and optimising the motor operating angle, specifically comprising the following steps: S2.1: IKOA parameter initialization settings Initialize the positions of N planets in the IKOA algorithm search space, that is, N initial switched reluctance motor advance angle values, X = {θ1, θ2, ..., θ N }, the initialization formula is as follows: X i =X i,lb +r0×(X i,ub -X i,lb ) (1) Where, X i,ub With X i,lb are the upper and lower bounds of the search space of the advance angle respectively; r0 is a random number between 0 and 1, i∈(1~N), 1, 2, 3…N is the sequence number of the advance angle θ; Initialize orbital eccentricity e and orbital period T i , initialized before the optimization begins, orbital eccentricity e and orbital period T i The initialization formula is as follows: e i ＝r1 (2) T i ＝|r n | (3) Where r1 is a random number between 0 and 1; r n is a random number based on normal distribution; i∈(1~N); The advance angle is the core parameter that affects the performance of the switched reluctance motor. The advance angle value of the switched reluctance control is set by the host computer, and the real-time speed n of the switched reluctance motor at each advance angle is sampled in turn. RPM and operating efficiency η RPM , based on n RPM and η RPM Calculate the fitness value J to determine the optimal advance angle position X S (t), as shown below: J=a RPM ·or RPM +(1-a RPM )·n RPM (4) Where, α RPM The control factor of the performance target for the lead angle calibration is used. By adjusting the factor, the operating target of the switched reluctance motor can be adjusted to operating efficiency or speed at a fixed output power. S2.2: Calculate the gravity of the IKOA planets Calculate the advance angle relative to the sun's gravitational force F g , the sun is the optimal advance angle in the angle position control of the switched reluctance motor, and the gravitational force is as follows: Where t represents the current iteration number; μ(t) is a function that decreases exponentially with the iteration number t, and is used for the search accuracy of angle optimization control, and is defined as shown in Equation (12); and Represent the optimal advance angle X s and advance angle X i The quality of M s and m s The normalized value of is calculated and normalized according to formula (8) and (9); r2 is a random number between 0 and 1; is the optimal advance angle X s and advance angle X i The Euclidean distance of R is obtained from formula (7); i Expressed as Euclidean normalized distance; ε is a small value to avoid division by zero; the optimal advance angle X s and advance angle X i The Euclidean distance and related parameters can be calculated by formulas (6) to (12), as shown below: Where r3 is a random number in the range of 0 to 1; M S Expressed as the optimal advance angle X s Mass; m i Indicated as X i The mass of γ is a fixed value; μ0 is an initial value; T max Indicates the maximum number of iterations; S2.3: Calculate the orbital velocities of planets in IKOA The speed of the advance angle change is calculated based on the distance from the optimal advance angle. The advance angle speed increases as the advance angle gets closer to the optimal advance angle and decreases as the distance from the optimal advance angle gets farther away. The advance angle speed is calculated using equations (13) to (17) as follows: l=U×M×L (14) M＝(r4×(1-r5)+r5) (16) Where V i (t) represents the current advance angular velocity; r4, r5, r6 and r7 are random numbers ranging from 0 to 1; X a and X b are two random solutions in the current solution; a i (t) is the semi-major axis of the ith advance angle elliptical orbit, which can be calculated by formula (21); α t is the advance angular velocity update step size, which can be calculated by formula (22); R i-norm (t) represents X s With X i The normalized Euclidean distance between them can be calculated by formula (23). The calculation formulas are as follows: Where, α max and α min are the upper and lower limits of the step length respectively; The purpose of formula (23) is to calculate the percentage of steps that each advance angle changes; if R i-norm (t)≤0.5, the advance angle is close to the optimal advance angle, and the speed will be increased to prevent drifting towards the optimal advance angle due to the huge attraction of the optimal advance angle. Otherwise, the advance angle will slow down; S2.4: Escape from IKOA local optimization Through adaptive adjustment, the model can achieve an effective balance between global search and local refinement, improving the overall performance of the algorithm. r-norm As shown in formula (24): P r-norm =0.7-0.4(T max -t) / T max (24) S2.5: Update the advance angle position in IKOA In IKOA, exploration operations are simulated when the advance angle is far from the optimal advance angle, while mining operations are realized when the advance angle is close to the optimal advance angle. The position update is as follows: Where r8 is a random number between 0 and 1; The position of the advance angle is continuously updated to simulate the natural change of the distance between the optimal advance angle and the advance angle over time; when the advance angle is getting closer to the optimal advance angle, the mining operator is activated to increase the convergence speed; and when the optimal advance angle is getting farther away from the advance angle, the exploration operator is activated to minimize falling into the local optimal state, as shown in the following formula: Where h is the adaptive factor that controls the distance between the optimal advance angle of the tth iteration and the current advance angle, and is calculated as follows: Where r9 is a randomly generated number from a normal distribution; η is a linearly decreasing factor from 1 to -2, calculated as follows: η=(a2-1)×r 10 +1 (28) Where r 10 is a random number between 0 and 1; a2 is the control parameter of η, which is calculated as follows: Where, is the parameter for controlling the number of cycles; % is the remainder operation; When the number of iterations of the above-mentioned IKOA algorithm reaches the set value and tracks near the current optimal advance angle, in order to further improve the accuracy of the algorithm for the optimal advance angle, an optimized variable step-size perturbation algorithm is used to perform a small-scale local search to find the global optimal advance angle.

5. The method for optimizing and calibrating the angle position control of a switched reluctance motor according to claim 4, wherein: Step S3: Real-time fine-tuning of the motor operation, including The local optimal advance angle obtained in step S2 is input into the optimized variable-step disturbance observation algorithm. Small angle disturbances are applied in real time through the controller, and the speed stability and operating efficiency of the motor are monitored using sensors. Based on the feedback data, it is determined whether the disturbance direction optimizes the performance, and the optimal calibration point is gradually adjusted iteratively. Finally, the roughness of the initial search phase is overcome, the accuracy of the calibration point is further improved, and the dynamic changes of load mutations and temperature drifts during motor operation are adapted to ensure that the calibration point is always close to the actual optimal state. The specific steps are as follows: By sampling and calculating the output speed, efficiency and change rate of the switched reluctance motor system in real time, the disturbance step size is dynamically adjusted to improve the corresponding speed of angle calibration. The disturbance process is shown in the following formula: Where n SRM is the output speed of the switched reluctance motor system measured; n SRM (t) and n SRM (t-1) are the speed values ​​before and after the t-th disturbance; Δn SRM The speed increment caused by the duty cycle change from iteration number t-1 to iteration number t; Δη RPM (t) is the efficiency increment caused by the duty cycle change from time t-1 to iteration t; Δθ(t) is the step size of the advance angle change at iteration t; Δθ step is the fixed change of the advance angle. If Δη SRM Is positive, then continue to perturb in the current direction; if Δη SRM If it is negative, the disturbance is in the opposite direction. The optimal advance angle optimized in steps S2 and S3 is transmitted to the switched reluctance motor controller via the host computer. The controller generates a corresponding switching signal based on the calibrated advance angle and the current switched reluctance motor operating parameters, driving the power conversion unit to adjust the power-on timing of each phase winding. At the same time, the external sensor system continuously collects efficiency and speed data of the switched reluctance motor and feeds it back to the host computer IKOA-OIP&O advance angle calibration algorithm to form a closed-loop control and fine-tune the deviation of the calibration point in real time.

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