Satellite brushless motor PID parameter optimization method and system based on weighted vector mean value

Abstract

The invention provides an on-satellite brushless motor PID (Proportion Integration Differentiation) parameter optimization method and system based on a weighted vector mean algorithm. The method comprises the following steps: firstly, acquiring a step response curve of a motor, and encoding KP, KI and KD to obtain encoded chromosomes; secondly, in the encoding process, a chaotic mapping mode is adopted, relevant parameters are subjected to normalization processing, and then corresponding codes are obtained according to the chaotic mapping principle; the genetic algorithm is a weighted vector mean algorithm, and the algorithm extracts a rule based on a mean value from weighted mean values of a group of random vectors. In addition, when the weighted average value of the vector is calculated, a wavelet factor is considered in weight setting, and modeling is carried out on a PID parameter signal. And finally, when the operation result of the algorithm is converged to the specified precision or the iteration step number reaches the preset maximum value, the operation of the algorithm is ended, and the optimized PID parameter is obtained, so that the setting of the PID parameter is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of motors, and specifically, to a method and system for optimizing PID parameters of a space brushless motor based on weighted vector mean value. Background Art

[0002] In the current era of rapid technological development, space brushless motors, with their many advantages, are showing an increasingly wide range of applications in various space-related fields. Whether it is the extremely challenging space exploration activities, the key power support for satellite platform operation, or in many aspects of the entire aerospace field, space brushless motors play an indispensable and important role, providing strong and reliable power guarantee for the smooth progress of various tasks. However, with the continuous progress of technology, the application scenarios are developing towards more complex and diverse directions, and at the same time, the requirements for the operating accuracy of motors are also continuously increasing. In this context, space brushless motors require a more stable and efficient PID control system to ensure their correct operation.

[0003] The PID control system is a basic feedback control method. It can, during the control process, adjust the output of the controller in real time and accurately according to the actual operating state of the system, so as to prompt the entire system to gradually approach and finally reach the desired ideal state. However, when applying the PID control system to the space environment, many difficult problems are faced, and the problem of tuning PID parameters is particularly prominent. First of all, different space environments have their own unique physical characteristics, and this difference directly leads to the fact that in different space environments, the setting of PID parameters cannot adopt a unified standard, but must be optimized and adjusted specifically according to the specific environmental characteristics.

[0004] In addition, the space environment is much harsher than the earth's surface environment, and there are various interference factors. Such as temperature changes, radiation particles, etc. These factors have a negative impact on the stability and response speed of the PID control system. These complex and severe problems make the tuning of PID parameters of space brushless motors extremely difficult. Traditional methods relying on manual experience or simple fixed parameter adjustment methods are no longer able to meet the urgent needs of current space brushless motors for high-precision and high-stability operation. Therefore, in order to ensure that space brushless motors can operate stably and efficiently in the complex and harsh space environment, it is very important to deeply, scientifically and effectively optimize their PID parameters.

[0005] Through the retrieval of patent documents, it is found that the invention patent with the application number CN201711467208.4 discloses a method for controlling a single-phase brushless DC motor by real-time closed-loop adjustment of the internal power factor angle. This method calculates the magnitude of the internal power factor angle in real time based on the phase difference between the Hall signal and the phase current, and realizes closed-loop adjustment by controlling the phase of the phase voltage. However, this method shows a relatively high complexity in the actual implementation process, and has relatively strict requirements for the real-time performance of the system.

[0006] In summary, aiming at the problems of the above-mentioned existing technologies, researching a method and system for optimizing the PID parameters of the on-board brushless motor based on the weighted vector mean has become a key task that needs to be solved urgently at present. Summary of the Invention

[0007] Aiming at the defects in the existing technologies, the purpose of the present invention is to provide a method and system for optimizing the PID parameters of the on-board brushless motor based on the weighted vector mean.

[0008] According to a method for optimizing the PID parameters of the on-board brushless motor based on the weighted vector mean provided by the present invention, it includes the following steps:

[0009] Step S1, obtain the step response curve of the motor, encode the PID parameters of the PID controller to generate chromosomes;

[0010] Step S2, decode the chromosomes into initial PID parameters, and calculate the fitness value of the initial PID parameters by using the fitness function;

[0011] Step S3, based on the initial PID parameters and the fitness value, adopt the genetic algorithm and the weighted vector mean algorithm to output the optimized PID parameters.

[0012] Preferably, in step S1, the PID parameters include the proportionality coefficient K P , the integral coefficient K I and the differential coefficient K D .

[0013] Preferably, step S1 includes the following sub-steps:

[0014] Step S1.1, set the ranges of the proportionality coefficient K P , the integral coefficient K I and the differential coefficient K D ;

[0015] Step S1.2, perform normalization processing;

[0016] Step S1.3, perform chaotic mapping;

[0017] Step S1.4, encode into chromosomes.

[0018] Preferably, in step S1.1, set K p ∈[a, b], where a is the minimum value of K P and b is the maximum value of K P ; set K I ∈[c, d], where c is the minimum value of K I and d is the maximum value of K I ; set K D ∈[e, f], where e is the minimum value of K D and f is the maximum value of K D .

[0019] Preferably, in step S1.2, normalize K P , K I , and K D respectively to obtain the normalized X KP , X KI , and X KD . The normalization formula is:

[0020]

[0021]

[0022] Preferably, in step S1.3, perform chaotic mapping on X KP , X KI , and X KD . The formula for chaotic mapping is:

[0023]

[0024]

[0025] where w is a parameter controlling the mapping effect, and 0 < w < 0.5;

[0026] In step S1.4, encode the X KP+1 , X KI+1 , and X KD+1 after chaotic mapping into chromosomes.

[0027] Preferably, in step S2, the fitness function is:

[0028]

[0029] where t is time and e(t) is the feedback deviation. The fitness function calculates the fitness value based on the decoded initial PID parameters.

[0030] Preferably, step S3 includes the following sub-steps:

[0031] Step S3.1, use the initial PID parameters as the initial solution;

[0032] Step S3.2, perform crossover and mutation operations on the initial solution through a genetic algorithm to generate a set of candidate PID parameters;

[0033] Step S3.3, use the fitness function to calculate the fitness value of each candidate PID parameter;

[0034] Step S3.4, randomly select multiple groups of vectors from the neighborhood or search space of each candidate PID parameter to generate new candidate PID parameters, and input them into the INFO algorithm to calculate the weighted average;

[0035] Step S3.5, the INFO algorithm uses the weighted average and the fitness value to weight the new candidate PID parameters to generate weighted PID parameters;

[0036] Step S3.6, select the one with the highest fitness value from the weighted PID parameters as the initial solution; repeat steps S3.2 to S3.6 until the weighted vector mean algorithm converges to the specified accuracy or the number of iteration steps reaches the preset maximum value, and output the optimized PID parameters.

[0037] Preferably, in step S3.5, the weight is calculated by the Morlet wavelet, and the weight calculation formula is as follows:

[0038]

[0039] where, w i is the weight coefficient, x i is the average value of the vector. In the form of the Morlet wavelet calculation formula, the vibration function is the oscillating part multiplied by cos in the Morlet wavelet. When calculating the weighted average X w , the weight coefficient w i is used for weighted calculation, and the calculation formula of INFO is as follows:

[0040]

[0041] where, N is the number of vectors, x i is the average value of the vector, ω is the dilation parameter, and X w is the weighted average.

[0042] The present invention also provides a satellite brushless motor PID parameter optimization system based on the weighted vector mean, including:

[0043] Module M1, obtain the step response curve of the motor, encode the PID parameters of the PID controller to generate chromosomes;

[0044] Module M2 decodes chromosomes into initial PID parameters and calculates the fitness values of the initial PID parameters using a fitness function;

[0045] Module M3 outputs optimized PID parameters using a genetic algorithm and a weighted vector mean algorithm based on the initial PID parameters and fitness values.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. The present invention uses a genetic algorithm and a weighted vector mean algorithm to obtain optimized PID parameters, reducing oscillation and overshoot phenomena in the system, and improving the anti-interference ability and control accuracy of the system.

[0048] 2. By optimizing the PID parameters, the present invention enables more accurate control of the motor speed and position, improving its response speed and accuracy.

[0049] 3. The present invention can not only improve the performance and system stability of the on-board brushless motor, but also has the advantages of high efficiency and easy implementation.

[0050] 4. By combining the translation and dilation of the Morlet wavelet oscillation function with a finite period, the present invention models the PID parameter signal. This function generates effective fluctuations during the optimization process, significantly improving the convergence speed and optimization accuracy of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0052] Figure 1 is the tuning flowchart of PID parameter optimization based on the weighted vector mean algorithm in the embodiment of the present invention;

[0053] Figure 2 is the system structure diagram of PID parameter optimization based on the weighted vector mean algorithm in the embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0054] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those of ordinary skill in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0055] Embodiment 1:

[0056] Figure 1This is the tuning flowchart for PID parameter optimization based on the weighted vector mean algorithm in the embodiments of the present invention.

[0057] As Figure 1 shown, this embodiment provides a method for optimizing the PID parameters of a brushless motor on a satellite based on the weighted vector mean, including the following steps:

[0058] Step S1: Obtain the step response curve of the motor, encode the PID parameters of the PID controller, and generate chromosomes.

[0059] In this embodiment, the PID parameters include the proportional coefficient K P , the integral coefficient K I , and the derivative coefficient K D .

[0060] Specifically, step S1 includes the following sub-steps:

[0061] Step S1.1: Set the ranges of the proportional coefficient K P , the integral coefficient K I , and the derivative coefficient K D .

[0062] Specifically, set K p ∈[a, b], where a is the minimum value of K P , and b is the maximum value of K P ; set K I ∈[c, d], where c is the minimum value of K I , and d is the maximum value of K I ; set K D ∈[e, f], where e is the minimum value of K D , and f is the maximum value of K D .

[0063] Step S1.2: Perform normalization processing;

[0064] Specifically, perform normalization processing on K P , K I , and K D respectively to obtain the normalized X KP , X KI , and X KD . The normalization formula is:

[0065]

[0066] Step S1.3: Perform chaotic mapping;

[0067] Specifically, map X KP , X KI , and X KDPerform a chaotic mapping, and the formula for the chaotic mapping is:

[0068]

[0069] where w is a parameter that controls the mapping effect, and 0 < w < 0.5.

[0070] Step S1.4, encode it into a chromosome.

[0071] Specifically, encode the X after chaotic mapping KP+1 、X KI+1 and X KD+1 into a chromosome.

[0072] Step S2, decode the chromosome into the initial PID parameters, and calculate the fitness value of the initial PID parameters using the fitness function.

[0073] In this embodiment, the fitness function is:

[0074]

[0075] where t is time and e(t) is the feedback deviation, and the fitness function calculates the fitness value based on the decoded initial PID parameters.

[0076] Step S3, according to the initial PID parameters and the fitness value, adopt the genetic algorithm and the weighted vector mean algorithm to output the optimized PID parameters.

[0077] Specifically, step S3 includes the following sub-steps:

[0078] Step S3.1, use the initial PID parameters as the initial solution;

[0079] Step S3.2, perform crossover and mutation operations on the initial solution through the genetic algorithm to generate a set of candidate PID parameters;

[0080] Step S3.3, calculate the fitness value of each candidate PID parameter using the fitness function;

[0081] Step S3.4, randomly select multiple groups of vectors from the neighborhood or search space of each candidate PID parameter to generate new candidate PID parameters, and input them into the INFO algorithm to calculate the weighted average;

[0082] Step S3.5, the INFO algorithm uses the weighted average and the fitness value to weight the new candidate PID parameters to generate weighted PID parameters;

[0083] In this embodiment, the fitness value is used to evaluate the quality of the PID parameters. The PID parameter combination with a higher fitness value has a greater weight, and the PID parameter combination with a lower fitness value has a smaller weight.

[0084] Specifically, the weights are calculated by Morlet wavelets, and the weight calculation formula is as follows:

[0085]

[0086] where w i is the weight system, x i is the average value of the vector. In the form of the Morlet wavelet calculation formula, the vibration function is the oscillating part multiplied by cos in the Morlet wavelet. When calculating the weighted average value X w , the weight coefficient w i is used for weighted calculation.

[0087] The calculation formula of INFO is as follows:

[0088]

[0089] where N is the number of vectors, x i is the average value of the vector, ω is the dilation parameter, and X w is the weighted average value.

[0090] Step S3.6: Select the one with the highest fitness value from the weighted PID parameters as the initial solution; repeat steps S3.2 to S3.6 until the weighted vector mean algorithm converges to the specified accuracy or the number of iterations reaches the preset maximum value, and output the optimized PID parameters.

[0091] In this embodiment, the specified accuracy is 2%, and the preset maximum value is 1000.

[0092] In the genetic algorithm of this embodiment, the weighted average idea is used for the entity structure, and the position of the vector is updated through three core processes. The update rule stage generates new vectors based on the law of the mean and convergence acceleration. The vector combination stage creates a combination of the obtained vectors and the update rules, and the local search stage helps the algorithm avoid low-precision solutions and improve the utilization rate and convergence.

[0093] Embodiment 2:

[0094] Figure 2 This is the system structure diagram of the PID parameter optimization based on the weighted vector mean algorithm in the embodiment of the present invention.

[0095] As Figure 2As shown in the figure, this embodiment provides a on-orbit brushless motor PID parameter optimization system based on weighted vector mean. The on-orbit brushless motor PID parameter optimization system based on weighted vector mean can be implemented by executing the process steps of the on-orbit brushless motor PID parameter optimization method based on weighted vector mean. That is, those skilled in the art can understand the on-orbit brushless motor PID parameter optimization method based on weighted vector mean as the preferred implementation manner of the on-orbit brushless motor PID parameter optimization system based on weighted vector mean.

[0096] Specifically, the on-orbit brushless motor PID parameter optimization system based on weighted vector mean includes:

[0097] Module M1, which acquires the step response curve of the motor, encodes the PID parameters of the PID controller, and generates chromosomes;

[0098] Module M2, which decodes the chromosomes into initial PID parameters and calculates the fitness value of the initial PID parameters using the fitness function;

[0099] Module M3, which outputs the optimized PID parameters according to the initial PID parameters and the fitness value, using the genetic algorithm and the weighted vector mean algorithm.

[0100] Those skilled in the art know that in addition to implementing the system and its various devices, modules, and units provided by the present invention in the form of pure computer-readable program code, the method steps can be logically programmed to enable the system and its various devices, modules, and units provided by the present invention to be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers, etc., to achieve the same functions. Therefore, the system and its various devices, modules, and units provided by the present invention can be regarded as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be regarded as the structures within the hardware component; the devices, modules, and units for implementing various functions can also be regarded as either software modules for implementing the method or structures within the hardware component.

[0101] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above specific implementation manners, 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. Without conflict, the embodiments of the present application and the features in the embodiments can be combined arbitrarily with each other.

Claims

1. A method for optimizing PID parameters of a brushless motor on a satellite based on weighted vector mean, characterized in that: The steps include: Step S1, obtaining a step response curve of the motor, encoding PID parameters of the PID controller, and generating chromosomes; Step S2, decoding the chromosome into initial PID parameters, and calculating the fitness value of the initial PID parameters using a fitness function; Step S3, according to the initial PID parameters and the fitness value, a genetic algorithm and a weighted vector mean algorithm are used to output optimized PID parameters.

2. According to claim 1, a method for optimizing PID parameters of a brushless motor on a satellite based on a weighted vector mean, characterized in that: In step S1, the PID parameters include the proportional coefficient K P , integral coefficient K I and the differential coefficient K D .

3. The method for optimizing PID parameters of a brushless motor on a satellite based on a weighted vector mean according to claim 2, characterized in that: The step S1 includes the following sub-steps: Step S1.1, setting the proportionality coefficient K P , the integral coefficient K I and the differential coefficient K D scope; Step S1.2, performing normalization processing; Step S1.3, performing chaotic mapping; Step S1.4, encoding as chromosomes.

4. The method for optimizing PID parameters of a brushless motor on a satellite based on weighted vector mean according to claim 3, characterized in that: In step S1.1, set K p ∈[a,b], where a is K P The minimum value of K P The maximum value of K I ∈[c,d], where c is K I The minimum value of K I The maximum value of K D ∈[e,f], where e is K D The minimum value of K D The maximum value of .

5. The method for optimizing PID parameters of a brushless motor on a satellite based on weighted vector mean according to claim 4, characterized in that: In step S1.2, K P , K I , K D After normalization, we get the normalized X KP , X KI , X KD , the normalized formula is:

6. The method for optimizing PID parameters of a brushless motor on a satellite based on weighted vector mean according to claim 5, characterized in that: In step S1.3, the X KP , X KI and X KD Perform chaos mapping, the formula of the chaos mapping is: Among them, w is the parameter that controls the mapping effect, 0 <w<0.5; In step S1.4, the chaotic mapped X KP+1 , X KI+1 and X KD+1 Encoded as chromosomes.

7. The method for optimizing PID parameters of a brushless motor on a satellite based on weighted vector mean according to claim 1, characterized in that: In step S2, the fitness function is: Wherein, t is time, e(t) is feedback deviation, and the fitness function calculates the fitness value based on the decoded initial PID parameters.

8. The method for optimizing PID parameters of a brushless motor on a satellite based on weighted vector mean according to claim 1, characterized in that: The step S3 includes the following sub-steps: Step S3.1, using the initial PID parameters as an initial solution; Step S3.2, performing crossover and mutation operations on the initial solution through a genetic algorithm to generate a set of candidate PID parameters; Step S3.3, using the fitness function to calculate the fitness value of each candidate PID parameter; Step S3.4, randomly selecting multiple groups of vectors from the neighborhood or search space of each candidate PID parameter, generating new candidate PID parameters, and inputting them into the INFO algorithm to calculate the weighted average; Step S3.5, the INFO algorithm uses the weighted average value and the fitness value to weight the new candidate PID parameters to generate weighted PID parameters; Step S3.6, select the one with the highest fitness value from the weighted PID parameters as the initial solution; repeat steps S3.2 to S3.6 until the weighted vector mean algorithm converges to the specified accuracy or the number of iterations reaches the preset maximum value, and output the optimized PID parameters.

9. The method for optimizing PID parameters of a brushless motor on a satellite based on weighted vector mean according to claim 6, characterized in that: In step S3.5, the weight is calculated by Morlet wavelet, and the weight calculation formula is as follows: Among them, w i is the weight coefficient, x i is the average value of the vector. In the form of the Morlet wavelet calculation formula, the vibration function is the oscillation part of the Morlet wavelet multiplied by cosine. w When the weight coefficient w i For weighted calculations, The calculation formula of INFO is as follows: Where N is the number of vectors, x i is the average value of the vector, ω is the expansion parameter, X w is the weighted average.

10. A brushless motor PID parameter optimization system based on weighted vector mean, characterized in that: include: Module M1, obtains the step response curve of the motor, encodes the PID parameters of the PID controller, and generates chromosomes; Module M2, decoding the chromosome into initial PID parameters, and calculating the fitness value of the initial PID parameters using a fitness function; Module M3, based on the initial PID parameters and the fitness value, uses a genetic algorithm and a weighted vector mean algorithm to output optimized PID parameters.

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