PID control based on BP neural network optimized by Q⁃learning for speed control of BLDCM

被引：0

作者：

Wang H.-Z. ^{[1
,2
]}

Wang T.-T. ^{[1
]}

Hu H.-S. ^{[3
]}

Lu X.-F. ^{[3
]}

机构：

[1] School of Mechatronic Engineering, Changchun University of Technology, Changchun

[2] School of Computer Science and Engineering, Changchun University of Technology, Changchun

[3] School of Computer Science and Engineering, Jilin University of Architecture and Technology, Changchun

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2021年 / 51卷 / 06期

关键词：

Back propagation neural network(BPNN); Brushless direct current motor(BLDCM); Control theory and control engineering; Proportion integration differentiation controller; Q-learning;

D O I：

10.13229/j.cnki.jdxbgxb20200580

中图分类号：

学科分类号：

摘要：

In order to improve the stability of Brushless DC Motor (BLDCM), a method of Q-learning algorithm optimized BP neural network PID controller (QBP-PID) is proposed. QBP-PID uses BP Neural Network (BPNN) to adjust the PID gain, and then optimizes the key weights in BPNN by modifying the weight momentum factor through Q-learning. Therefore the controller has better learning and online correction abilities, and the BLDCM can achieve better control effect. The simulation results show that QBP-PID has better adaptive ability, anti-interference ability and stronger robustness than the traditional PID, Fuzzy PID (Fuzzy-PID) and BP neural network PID (BP-PID) controllers. © 2021, Jilin University Press. All right reserved.

引用

页码：2280 / 2286

页数：6

共 15 条

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