永磁直线伺服系统的自适应改进Elman神经网络积分反推控制

doi:10.19595/j.cnki.1000-6753.tces.181656

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Abstract The permanent magnet linear synchronous motor (PMLSM) servo system has the uncertainties such as parameter variations, external disturbance and friction. Therefore, a control scheme combining integral backstepping control and adaptive modified Elman neural network was adopted. Firstly, for the nonlinear characteristics of PMLSM servo system, the integral backstepping control method was used to design the virtual control function through the step-by-step modification algorithm to realize the global adjustment and position tracking of the system. Secondly, an adaptive modified Elman neural network was designed to estimate the uncertainty in the system, and the on-line parameter learning law of the neural network was derived by the adaptive law based on Lyapunov function. Thus, the system had the ability to adapt to the time-varying characteristics and overcome the influence of uncertainty on the system, thereby improving the robustness of the system. Finally, the experimental results show that the proposed control scheme is effective, and the tracking performance and robust performance of the system are obviously improved.

Key words： Permanent magnet liner synchronous motor integral backstepping control modified Elman neural network uncertainties

Received: 22 October 2018 Published: 17 January 2020

PACS:	TP273
	TM351

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	Zhao Ximei
	Fu Dongxue
	Jin Yangyang

Cite this article:

Zhao Ximei,Fu Dongxue,Jin Yangyang. Adaptive Modified Elman Neural Network Integral Backstepping Control for Permanent Magnet Linear Servo System[J]. Transactions of China Electrotechnical Society, 2020, 35(2): 266-273.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.181656 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I2/266

[1] Panah P G, Ataei M, Mirzaeian B, et al.A robust adaptive sliding mode control for PMLSM with variable velocity profile over wide range[J]. Research Journal of Applied Sciences Engineering & Technology, 2015, 10(9): 997-1006.
[2] 王丽梅, 张宗雪. H型精密运动平台交叉耦合模糊PID同步控制[J]. 沈阳工业大学学报, 2018, 40(1): 1-5.
Wang Limai, Zhang Zongxue.Cross-coupled fuzzy PID synchronous control for H-type precision motion platform[J]. Journal of Shenyang University of Technology, 2018, 40(1): 1-5.
[3] 朱国昕, 雷鸣凯, 赵希梅. 永磁同步电机伺服系统自适应迭代学习控制[J].沈阳工业大学学报, 2018, 40(1): 6-11.
Zhu Guoxin, Lei Mingkai, Zhao Ximei.Adaptive iterative learning control for permanent magnet synchronous motor servo system[J]. Journal of Shenyang University of Technology, 2018, 40(1): 6-11.
[4] 赵希梅, 吴勇慷. 基于多阶段速度规划的PMLSM自适应反推滑模控制[J]. 电工技术学报, 2018, 33(3): 662-669.
Zhao Ximei, Wu Yongkang.Adaptive backstepping siding mode control for PMLSM based on multi- segment velocity planning[J]. Transactions of China Electrotechnical Society, 2018, 33(3): 662-669.
[5] Hocine A, Benalia A.Sensorless fault tolerant control for induction motors using: backstepping strategy and sliding mode observer[C]//8th International Con- ference on Modelling, Identification and Control, Algiers, 2017: 1020-1028.
[6] 余洋, 郭旭东, 郑晓明, 等. 基于反推控制的机械弹性储能永磁同步电机最大转矩电流比控制[J]. 电工技术学报, 2017, 32(22): 82-90.
Yu Yang, Guo Xudong, Zheng Xiaoming, et al.Back- stepping control based maximum torque per ampere control of permanent magnet synchronous motor for mechanical elastic energy storage[J]. Transactions of China Electrotechnical Society, 2017, 32(22): 82-90.
[7] Lin Chih-Hong, Lin Chih-Peng.Integral backstepping control for a permanent magnet synchronous motor using adaptive recurrent neural network uncertainty observer[J]. International Journal of Engineering & Technology Innovation, 2012, 1(1): 777-780.
[8] Shao Mingling, Yu Haisheng, Yu Jinpeng, et al.Position control of permanent magnet synchronous motor speed sensorless servo system via back- stepping[C]//27th Chinese Control and Decision Conference, Qingdao, 2015: 4089-4094.
[9] Yu Jinpeng, Shi Peng, Dong Wenjie, et al.Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors[J]. IEEE Transactions on Neural Networks & Learning Systems, 2015, 26(3): 640-645.
[10] Wang Limei, Li Xiaobin.Position sensorless control for PMLSM using Elman neural network[C]//Inter- national Conference on Information Engineering and Computer Science, Wuhan, 2009: 1-4.
[11] Lin Chih-Hong.Dynamic control of V-belt con- tinuously variable transmission-driven electric scooter using hybrid modified recurrent legendre neural network control system[J]. Nonlinear Dynamics, 2015, 79(2): 787-808.