Closed-loop control method and system of on-satellite brushless motor based on differential evolution

Abstract

The invention provides a closed-loop control method and system for an on-satellite brushless motor based on differential evolution, and the method comprises the steps: obtaining a step response curve, carrying out the differential mapping coding of a PID parameter, obtaining corresponding differential coding values, connecting the differential coding values in series to form a chromosome representing a possible solution, and forming an initial population. Calculating a fitness parameter of the initial population according to a fitness function; inputting the fitness parameter into a genetic algorithm for calculation, judging whether the current genetic algebra reaches a preset maximum value or not, if so, outputting a current optimal solution, and performing setting based on the PID parameter of the optimal solution; and if not, adding one to the population number, and repeating the steps. According to the chaotic coding method, the chaotic coding is carried out by utilizing the property of a chaotic system, the anti-interference capability is relatively high, the noise interference in the signal transmission process can be effectively reduced, and the reliability of the signal is improved.

Description

Technical Field

[0001] The present invention relates to the field of motor technology, and in particular to a closed-loop control method and system for a brushless motor on a satellite based on differential evolution. Background Art

[0002] At present, brushless motors on satellites are increasingly used in various fields, such as space exploration, satellite platforms, and aerospace. To meet complex scenarios and high-precision requirements, these motors require a more stable and efficient PID control system to ensure normal operation. The PID control system is a commonly used feedback control method that can adjust the output in real time to achieve the desired state. However, in the satellite environment, PID parameter setting is challenging and needs to be optimized according to different environments. It is affected by interference such as temperature changes and radiation, and the stability and response speed of the PID system are impaired. Traditional adjustment methods are difficult to cope with, so the optimization of the PID parameters of brushless motors is crucial.

[0003] Patent document CN113794423A (application number: CN202110888439.2) discloses a method for debugging brushless motor PI parameters. This method first performs rough adjustment and then fine adjustment. After the rough adjustment is completed, the proportional term coefficient and the integral term coefficient are fine-tuned according to the system's requirements for rapidity and error. Manual adjustment is required, and the accuracy error of the adjustment is large. In contrast, this method has good adaptability and robustness, can track signals more accurately, and reduce the steady-state error of the system.

[0004] Patent document CN112366989A (application number: CN202011303450.X) discloses a brushless DC motor control method based on parameter identification. This method detects the resistance and inductance values ​​of the three-phase windings of the motor, stores them in registers, and brings the difference between the identified resistance and inductance values ​​and the initial values ​​into the motor rotor position estimation equation to compensate for the commutation error. However, this method requires more computing resources and time for parameter identification and compensation operations.

[0005] Patent document CN115411990A (application number: CN202211153029.4) discloses a fuzzy PID control algorithm for a brushless DC motor. The controller mainly consists of three parts: fuzzification, fuzzy logic reasoning, and defuzzification. A triangular membership function is designed to realize PID control, but the rigid interference capability of the triangular code is low.

[0006] Patent document CN110504874B (application number: CN201910766446.8) discloses a brushless DC motor boost speed closed-loop control method, which adopts two modes, namely, after providing voltage through a DC-DC variable controller, using a subsequent power converter to control the speed, and directly adjusting the output of the converter to control the speed. However, this method has a complex control structure, is difficult to adjust parameters, and has high requirements on hardware, which increases the complexity and cost of system design.

[0007] Patent document CN107612433A (application number: CN201711091392.7) discloses a single closed-loop control method for a brushless motor based on an improved speed closed-loop control algorithm, including a given speed, a feedback speed, a calculated speed deviation, an improved speed closed-loop control modulation and a control PWM signal. However, this method is highly dependent on sensors, has a complex system and high cost. Summary of the invention

[0008] In view of the defects in the prior art, an object of the present invention is to provide a closed-loop control method and system for a brushless motor on board a satellite based on differential evolution.

[0009] A closed-loop control method for a brushless motor on a satellite based on differential evolution is provided according to the present invention, comprising:

[0010] Step S1: Obtain a step response curve to perform differential mapping encoding on the PID parameters to obtain corresponding differential encoding values, and connect the differential encoding values ​​in series to form a chromosome representing a possible solution to form an initial population.

[0011] Step S2: Calculating the fitness parameter of the initial population according to the fitness function;

[0012] Step S3: Input the fitness parameter into the genetic algorithm for calculation, and determine whether the current genetic generation reaches the preset maximum value. If so, output the current optimal solution and adjust the PID parameters based on the optimal solution; if not, increase the population number by one and execute step S1.

[0013] Preferably, the differential mapping encoding in step S1 is performed by processing and mapping the three variables to obtain a suitable encoding form, as shown in the following formula:

[0014] X r =X(p r )+F / ∏[X(i r )-X(d r )]

[0015] Among them, X r is the generated candidate solution; X r 、X(i r )、X(d r) is the parameter vector of three randomly selected individuals, and the F factor calculation formula is as follows:

[0016] F=F0×2 λ

[0017] Wherein, F0 represents the basic parameter of the differential factor, and λ represents the gain of the differential factor.

[0018] Preferably, the adaptation function takes into account the response speed and the overshoot, and the adaptation function is as follows:

[0019]

[0020] Where e(t) is the feedback deviation and u(t) is the controller output de(t) / dt, which is the rate of change of the feedback deviation.

[0021] Preferably, the preset maximum value is preferably 1000.

[0022] Preferably, the genetic algorithm uses a differential evolution algorithm, starting from a random initial solution and using differential mean information of a set of randomly selected vectors to move to the next solution;

[0023] The genetic algorithm uses a differential mean algorithm to select the next generation of individuals:

[0024]

[0025] Where X r (a) represents the value of individual r in generation a. r (a+1) represents the candidate solution generated by the mutation operation, and a is the number of generations of genetic mutation.

[0026] A closed-loop control system of a brushless motor on a satellite based on differential evolution is provided according to the present invention, comprising:

[0027] Module M1: Obtain the step response curve to perform differential mapping encoding on the PID parameters to obtain corresponding differential encoding values, and connect the differential encoding values ​​in series into a chromosome representing a possible solution to form an initial population.

[0028] Module M2: Calculate the fitness parameter of the initial population according to the fitness function;

[0029] Module M3: Input the fitness parameter into the genetic algorithm for calculation, and determine whether the current genetic generation reaches the preset maximum value. If so, output the current optimal solution and adjust the PID parameters based on the optimal solution; if not, increase the population number by one and trigger module M1.

[0030] Preferably, the differential mapping encoding in module M1 processes and maps the three variables to obtain a suitable encoding form, as shown in the following formula:

[0031] X r =X(p r )+F / ∏[X(i r )-X(d r )]

[0032] Among them, X r is the generated candidate solution; X r 、X(i r )、X(d r ) is the parameter vector of three randomly selected individuals, and the F factor calculation formula is as follows:

[0033] F=F0×2 λ

[0034] Wherein, F0 represents the basic parameter of the differential factor, and λ represents the gain of the differential factor.

[0035] Preferably, the adaptation function takes into account the response speed and the overshoot, and the adaptation function is as follows:

[0036]

[0037] Where e(t) is the feedback deviation and u(t) is the controller output de(t) / dt, which is the rate of change of the feedback deviation.

[0038] Preferably, the preset maximum value is preferably 1000.

[0039] Preferably, the genetic algorithm uses a differential evolution algorithm, starting from a random initial solution and using differential mean information of a set of randomly selected vectors to move to the next solution;

[0040] The genetic algorithm uses a differential mean algorithm to select the next generation of individuals:

[0041]

[0042] Where X r (a) represents the value of individual r in generation a. r (a+1) represents the candidate solution generated by the mutation operation, and a is the number of generations of genetic mutation.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The chaotic coding of the present invention utilizes the properties of the chaotic system for coding, has strong anti-interference ability, can effectively reduce noise interference in the signal transmission process, and improve the reliability of the signal.

[0045] 2. The present invention obtains the optimal PID parameter combination through differential coding and differential evolution, so that the closed-loop control system can have higher stability and performance under different working conditions, is suitable for different working conditions, has higher stability, enhances the adaptability and robustness of the system, can track signals more accurately, and reduce the steady-state error of the system.

[0046] 3. The present invention improves control accuracy and dynamic response by dynamically adjusting PID parameters, and the differential evolution algorithm has strong global search capability, has little impact on system parameter changes and environmental disturbances, and is flexible and practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0048] Figure 1 The system structure diagram of the PID parameter optimization based on the weighted vector mean algorithm of the present invention;

[0049] Figure 2 It is a schematic diagram of the tuning process of PID parameter optimization based on the weighted vector mean algorithm of the present invention. DETAILED DESCRIPTION

[0050] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0051] The present invention firstly P ,K I ,K D Differential mapping encoding is used to obtain the optimal parameters through genetic operations. The genetic algorithm uses a differential evolution algorithm. When the algorithm has converged to the specified accuracy or the number of iterations reaches the maximum value, the algorithm ends and the optimal solution is obtained, thereby realizing the tuning of PID parameters and improving the closed-loop control accuracy of the brushless motor on the satellite.

[0052] Embodiment 1

[0053] According to a closed-loop control method of a brushless motor on a satellite based on differential evolution provided by the present invention, Figure 1 and Figure 2 Shown include:

[0054] Step S1: Obtain the proportional coefficient K of the step response curve P , integral coefficient K I and the differential coefficient KD Perform differential encoding to obtain corresponding differential encoding values, and concatenate the differential encoding values ​​into a chromosome representing a possible solution to form an initial population. The encoding is a differential mapping, which processes and maps the three variables, and the corresponding encoding is:

[0055] X r =X(p r )+F / ∏[X(i r )-X(d r )]

[0056] Among them, X r is the generated candidate solution; X r 、X(i r )、X(d r ) is the parameter vector of three randomly selected individuals, and the F factor calculation formula is as follows:

[0057] F=F0×2 λ

[0058] Wherein, F0 represents the basic parameter of the differential factor, λ represents the gain of the differential factor, and the value of F is between [0, 2].

[0059] Step S2: Calculate the fitness parameters of the initial population according to the fitness function. Specifically, according to the encoding of the initial population, use the fitness function to evaluate the quality of each solution. The fitness function takes into account the response speed and overshoot, and calculates the fitness of each solution according to the change rate of the feedback deviation to form a fitness value for selecting the optimal solution. The fitness function is as follows:

[0060]

[0061] Where e(t) is the feedback deviation and u(t) is the controller output de(t) / dt, which is the rate of change of the feedback deviation.

[0062] Step S3: Input the fitness parameter into the genetic algorithm for calculation, and determine whether the current genetic generation has reached the preset maximum value. If so, output the current optimal solution, and adjust the PID parameters based on the optimal solution; if not, increase the population by one and execute step S1. The preset maximum value is preferably 1000, and the mutation generation of the genetic algorithm is a, and exits from a to 1000. Specifically, genetic operations and differential evolution generate new candidate solutions through the mutation and crossover operations of the differential evolution algorithm. The mutation operation uses a differential vector (i.e., the difference between random individuals is used to generate a new solution), and then a new population is generated through a crossover operation. According to the fitness evaluation result, select individuals with higher fitness for updating. Then, determine whether the current genetic generation has reached the preset maximum value, which is set to 1000 times. If yes, output the current optimal solution, and adjust the PID parameters based on the optimal solution; if not, increase the generation and return to step S1. PID parameter tuning includes tuning the control system based on the optimal PID parameters obtained by differential evolution algorithm optimization, so that the closed-loop control accuracy of the onboard brushless motor is improved.

[0063] The genetic algorithm uses a differential evolution algorithm, starting from a random initial solution and using the differential mean information of a set of randomly selected vectors to move to the next solution. The genetic algorithm uses a differential mean algorithm to select the next generation of individuals:

[0064]

[0065] Where X r (a) represents the value of individual r in generation a. r (a+1) represents the candidate solution generated by the mutation operation, a is the number of generations of genetic mutation; if u r (a+1) has better fitness, f(u r (a+1)) <f(u r (a)), then update the value of individual Xr to u r (a+1), X r (a+1)=u r (a+1), otherwise keep the value of the current individual Xr.

[0066] In the genetic algorithm, for each chromosome, a candidate solution is generated by using the mutation operation of the differential evolution algorithm, and then the candidate solution is cross-operated with the current chromosome to generate a new solution. According to the fitness evaluation result, the chromosomes in the population are updated, and individuals with higher fitness are retained to generate new vectors to improve utilization and convergence. When the algorithm repeats the mutation, crossover, fitness evaluation and selection operations until the stop condition is met (such as reaching the maximum number of iterations or fitness convergence). That is, it has converged to the specified accuracy or the number of iterations has reached the maximum value, then the algorithm ends and the optimal solution is obtained. Based on the output results, the PID parameters are adjusted.

[0067] The present invention aims to achieve PID parameter tuning, and obtain the optimal PID parameter combination through differential coding and differential evolution, so that the closed-loop control system can respond more stably and quickly under different working conditions, and improve the control performance of the system. According to real-time system performance feedback, the PID parameters are dynamically adjusted to make the system more adaptive and robust. In addition, the method can track the reference signal more accurately, reduce the steady-state error of the system, and improve the control accuracy and stability of the closed-loop control method of the brushless motor on the satellite.

[0068] Embodiment 2

[0069] The present invention also provides a closed-loop control system of an onboard brushless motor based on differential evolution. The closed-loop control system of an onboard brushless motor based on differential evolution can be realized by executing the process steps of the closed-loop control method of an onboard brushless motor based on differential evolution, that is, a person skilled in the art can understand the closed-loop control method of an onboard brushless motor based on differential evolution as a preferred implementation of the closed-loop control system of an onboard brushless motor based on differential evolution.

[0070] A closed-loop control system of a brushless motor on a satellite based on differential evolution is provided according to the present invention, comprising:

[0071] Module M1: Obtain the step response curve to perform differential mapping encoding on the PID parameters, obtain the corresponding differential encoding value, and concatenate the differential encoding value into a chromosome representing a possible solution to form an initial population. The differential mapping encoding in module M1 processes and maps the three variables to obtain a suitable encoding form, as shown in the following formula:

[0072] X r =X(p r )+F / ∏[X(i r )-X(d r )]

[0073] Among them, X r is the generated candidate solution; X r 、X(i r )、X(dr ) is the parameter vector of three randomly selected individuals, and the F factor calculation formula is as follows:

[0074] F=F0×2 λ

[0075] Wherein, F0 represents the basic parameter of the differential factor, and λ represents the gain of the differential factor.

[0076] Module M2: Calculate the fitness parameters of the initial population according to the fitness function; the fitness function takes into account the response speed and overshoot, and the fitness function is as follows:

[0077]

[0078] Where e(t) is the feedback deviation and u(t) is the controller output de(t) / dt, which is the rate of change of the feedback deviation.

[0079] Module M3: Input the fitness parameter into the genetic algorithm for calculation, and determine whether the current genetic generation reaches the preset maximum value. If so, output the current optimal solution and adjust the PID parameters based on the optimal solution; if not, the population number is increased by one and module M1 is triggered. The preset maximum value is preferably 1000. The genetic algorithm uses a differential evolution algorithm, starting from a random initial solution, and uses the differential mean information of a set of randomly selected vectors to move to the next solution; the genetic algorithm uses a differential mean algorithm to select the next generation of individuals:

[0080]

[0081] Where X r (a) represents the value of individual r in generation a. r (a+1) represents the candidate solution generated by the mutation operation, and a is the number of generations of genetic mutation.

[0082] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0083] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A closed-loop control method for a brushless motor on a satellite based on differential evolution, characterized in that: include: Step S1: Obtain a step response curve to perform differential mapping encoding on the PID parameters to obtain corresponding differential encoding values, and connect the differential encoding values ​​in series to form a chromosome representing a possible solution to form an initial population; Step S2: Calculating the fitness parameter of the initial population according to the fitness function; Step S3: Input the fitness parameter into the genetic algorithm for calculation, and determine whether the current genetic generation reaches the preset maximum value. If so, output the current optimal solution and adjust the PID parameters based on the optimal solution; if not, increase the population number by one and execute step S1.

2. The closed-loop control method of a brushless motor on a satellite based on differential evolution according to claim 1, characterized in that: In step S1, the differential mapping encoding processes and maps the three variables to obtain a suitable encoding form, as shown in the following formula: X r =X(p r )+F / ∏[X(i r )-X(d r )] Among them, X r is the generated candidate solution; X r 、X(i r )、X(d r ) is the parameter vector of three randomly selected individuals, and the F factor calculation formula is as follows: F=F0×2 λ Wherein, F0 represents the basic parameter of the differential factor, and λ represents the gain of the differential factor.

3. The closed-loop control method of a brushless motor on a satellite based on differential evolution according to claim 1, characterized in that: The adaptation function takes into account the response speed and overshoot, and the adaptation function is as follows: Where e(t) is the feedback deviation and u(t) is the controller output de(t) / dt, which is the rate of change of the feedback deviation.

4. The closed-loop control method of a brushless motor on a satellite based on differential evolution according to claim 1, characterized in that: The preset maximum value is preferably 1000.

5. The closed-loop control method of a brushless motor on a satellite based on differential evolution according to claim 1, characterized in that: The genetic algorithm uses a differential evolution algorithm, starting from a random initial solution and using the differential mean information of a set of randomly selected vectors to move to the next solution; The genetic algorithm uses a differential mean algorithm to select the next generation of individuals: Where X r (a) represents the value of individual r in generation a. r (a+1) represents the candidate solution generated by the mutation operation, and a is the number of generations of genetic mutation.

6. A closed-loop control system for a brushless motor on a satellite based on differential evolution, characterized in that: include: Module M1: Obtain the step response curve to perform differential mapping encoding on the PID parameters to obtain the corresponding differential encoding values, and connect the differential encoding values ​​in series into a chromosome representing a possible solution to form an initial population; Module M2: Calculate the fitness parameter of the initial population according to the fitness function; Module M3: Input the fitness parameter into the genetic algorithm for calculation, and determine whether the current genetic generation reaches the preset maximum value. If so, output the current optimal solution and adjust the PID parameters based on the optimal solution; if not, increase the population number by one and trigger module M1.

7. The closed-loop control system of the onboard brushless motor based on differential evolution according to claim 6, characterized in that: The differential mapping encoding in module M1 processes and maps the three variables to obtain a suitable encoding form, as shown below: X r =X(p r )+F / ∏[X(i r )-X(d r )] Among them, X r is the generated candidate solution; X r 、X(i r )、X(d r ) is the parameter vector of three randomly selected individuals, and the F factor calculation formula is as follows: F=F0×2 λ Wherein, F0 represents the basic parameter of the differential factor, and λ represents the gain of the differential factor.

8. The closed-loop control system of the onboard brushless motor based on differential evolution according to claim 6, characterized in that: The adaptation function takes into account the response speed and overshoot, and the adaptation function is as follows: Where e(t) is the feedback deviation and u(t) is the controller output de(t) / dt, which is the rate of change of the feedback deviation.

9. The closed-loop control system of the onboard brushless motor based on differential evolution according to claim 6, characterized in that: The preset maximum value is preferably 1000.

10. The closed-loop control system of the onboard brushless motor based on differential evolution according to claim 6, characterized in that: The genetic algorithm uses a differential evolution algorithm, starting from a random initial solution and using the differential mean information of a set of randomly selected vectors to move to the next solution; The genetic algorithm uses a differential mean algorithm to select the next generation of individuals: Where X r (a) represents the value of individual r in generation a. r (a+1) represents the candidate solution generated by the mutation operation, and a is the number of generations of genetic mutation.

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