基于自适应修正拉盖尔递归神经网络的永磁直线同步电机反推控制

doi:10.19595/j.cnki.1000-6753.tces.171239

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Abstract A backstepping control approach based on adaptive modified Laguerre recurrent neural network (AMLRNN) was proposed for permanent magnet linear synchronous motor (PMLSM) servo system which is vulnerable to influence of the uncertainties, such as parameter variations and nonlinear external disturbances. Firstly, the dynamic model of PMLSM with the uncertainties was established. And then, two optimal learning rates were derived by the on-line parameter training methodology based on the Lyapunov stability theorem to accelerate parameter convergence. This method can avoid the inherent problem of explosion of complexity and chattering phenomenon existed in the general adaptive backstepping control system, and make the system have good transient performance and robust performance. Finally, the experimental results confirm that the proposed scheme is effective and feasible. Compared with the general adaptive backstepping control system, the backstepping control system using AMLRNN has more superior control performance, and the position tracking error of system is obviously reduced.

Key words： Permanent magnet linear synchronous motor Laguerre recurrent neural network backstepping control Lyapunov stability tracking error

Received: 29 August 2017 Published: 24 May 2018

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TP273

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	Zhao Ximei
	Wu Yongkang

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Zhao Ximei,Wu Yongkang. Backstepping Control Based on Adaptive Modified Laguerre ecurrent Neural Network for Permanent Magnet Linear Synchronous Motor[J]. Transactions of China Electrotechnical Society, 2018, 33(10): 2392-2399.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.171239 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I10/2392

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