Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (6): 2280-2286.doi: 10.13229/j.cnki.jdxbgxb20200580

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

Hong-zhi WANG1,2(),Ting-ting WANG1,Huang-shui HU3(),Xiao-fan LU3   

  1. 1.School of Mechatronic Engineering，Changchun University of Technology，Changchun 130012，China
    2.School of Computer Science and Engineering，Changchun University of Technology，Changchun 130012，China
    3.School of Computer Science and Engineering，Jilin University of Architecture and Technology，Changchun 130114，China
  • Received:2020-07-30 Online:2021-11-01 Published:2021-11-15
  • Contact: Huang-shui HU E-mail:wanghongzhi@ccut.edu.cn;huhs08@163.com

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Abstract:

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.

Key words: control theory and control engineering, brushless direct current motor（BLDCM）, proportion integration differentiation controller, back propagation neural network（BPNN）, Q-learning

CLC Number: 

  • TP273

Fig.1

Equivalent circuit of BLDCM"

Fig.2

Control system of BP-PID"

Fig.3

Schematic diagram of Q-learning algorithm"

Fig.4

Framework of QBP-PID speed controller"

Table 1

Simulation parameters of BLDCM"

参 数数值
额定电压/V470
额定电流/A50
定子电阻相/Ω3
定子相电感/H0.001
电压常数/（V·r-1·min）0.1466
转矩常数/（N·m·A-1）1.4
转动惯量/（kg·m2·rad-1）0.0008
阻力因子/（N·m·s·rad-1）0.001
极对数P4

Fig.5

Speed response with no load"

Fig.6

Speed response with load"

Fig.7

Speed response with various speed"

1 张厚升,李震梅,边敦新,等.电动汽车用三相开绕组永磁同步电机的控制及容错运行[J].吉林大学学报:工学版,2020,50(3):784-795.
Zhang Hou-sheng, Li Zhen-mei, Bian Dun-xin, et al. Control and fault-tolerant operation of TPOW-PMSM for electric vehicle[J].Journal of Jilin University(Engineering and Technology Edition), 2020, 50(3): 784-795.
2 Yadav A K, Gaur P. An optimized and improved STF-PID speed control of throttle controlled HEV[J]. Arabian Journal for Science and Engineering, 2016, 41(9): 3749-3760.
3 Joseph G A, Sankaranarayanan V. A new electric braking system with energy regeneration for a BLDC motor driven electric vehicle[J]. Engineering Science and Technology, 2018, 21: 704-713.
4 Premkumar K, Manikandan B V. Fuzzy PID supervised online ANFIS based speed controller for brushless DC motor[J]. Neurocomputing, 2015, 157(6): 76-90.
5 Afrasiabi N, Yazdi M H. Sliding mode controller for DC motor speed control[J]. Global Journal of Science, Engineering, and Technology, 2013, 11: 45-50.
6 Gundogdu T, Komurgoz G. Self-tuning PID control of a brushless DC motor by adaptive interaction[J]. IEEJ Transaction on Electrical and Electronic Engineering, 2014, 9(4): 384-390.
7 Ramya A, Ahamed I, Balaji M. Hybrid self tuned fuzzy PID controller for speed control of brushless DC motor[J]. Automatika, 2016, 57(3): 672-679.
8 王欣, 梁辉, 秦斌. 基于OSELM的无刷直流电机无位置传感器控制[J]. 电机与控制学报, 2018, 22(11): 82-88.
Wang Xin, Liang Hui, Qin Bin. Sensorless control for brushless DC motors based on OSELM[J]. Electric Machines and Control, 2018, 22(11): 82-88.
9 李静, 左斌, 胡云安. 时延Elman递归神经网络及其在PMSM的混沌控制中的应用[J]. 吉林大学学报:工学版, 2008,38(2):214-219.
Li Jing, Zuo Bin, Hu Yun-an. Time delay Elman recurrent neural network and its application in PMSM chaos control[J]. Journal of Jilin University (Engineering and Technology Edition), 2008,38(2):214-219.
10 李国勇, 杨丽娟. 神经·模糊·预测控制及其Matlab实现[M]. 北京:电子工业出版社, 2013.
11 王同旭, 马鸿雁, 聂沐晗. 电梯用永磁同步电机BP神经网络PID调速控制方法的研究[J]. 电工技术学报, 2015():52-56.
Wang Tong-xu, Ma Hong-yan, Nie Mu-han. The research of PMSM BP neural network PID Control in elevator[J]. Transations of China Electrotechnical Society, 2015(Sup.1):52-56.
12 汪伟, 谭伦农, 杨泽斌, 等. 无轴承异步电机BP神经网络PID控制[J]. 电力电子技术, 2018, 52(11):32-35.
Wang Wei, Tan Lun-nong, Yang Ze-bin, et al. The PID control for a bearingless induction motor based on BP neural network[J]. Power Electronics, 2018, 52(11):32-35.
13 司彦娜, 普杰信, 臧绍飞. 基于残差梯度法的神经网络Q学习算法[J]. 计算机工程与应用, 2020, 56(18):137-142.
Si Yan-na, Pu Jie-xin, Zang Shao-fei. Neural network Q learning algorithm based on residual gradient method[J]. Computer Engineering and Applications, 2020, 56(18):137-142.
14 Watkins C J C H, Dayan P. Q-learning[J]. Machine Learning, 1992, 8(3/4): 279-292.
15 Hu H S, Wang T T, Zhao S Y, et al. Speed control of brushless direct current motor using a genetic algorithm-optimized fuzzy proportional integral differential controller[J]. Advances in Mechanical Engineering, 2019, 11(11): 1-13.
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