Title: Command filter-based adaptive neural control for permanent magnet synchronous motor stochastic nonlinear systems with input saturation

Authors: Yuxi Han; Jinpeng Yu; Zhen Liu; Lin Zhao

Addresses: College of Automation and Electrical Engineering, Qingdao University, Qingdao, 266071, China ' College of Automation and Electrical Engineering, Qingdao University, Qingdao, 266071, China ' College of Automation and Electrical Engineering, Qingdao University, Qingdao, 266071, China ' College of Automation and Electrical Engineering, Qingdao University, Qingdao, 266071, China

Abstract: For solving the problems of stochastic disturbance and input saturation existing in permanent magnet synchronous motors (PMSMs) drive systems, a command filter-based adaptive neural control method is proposed in this paper. Firstly, the neural networks technique is utilised to approximate unknown nonlinear functions. Then, the command filtered controller is constructed to avoid the 'explosion of complexity' inherent in the classic backstepping control and the error compensation mechanism is introduced to reduce the error caused by command filter. Moreover, the adaptive backstepping method is utilised to design controllers to assure that all signals are bounded in the closed-loop systems. Finally, the effectiveness of the approach is certified by the given simulation results.

Keywords: adaptive neural control; backstepping; permanent magnet synchronous motors; PMSMs; command filter; stochastic nonlinear systems.

DOI: 10.1504/IJMIC.2018.093544

International Journal of Modelling, Identification and Control, 2018 Vol.30 No.1, pp.38 - 47

Received: 17 Jun 2017
Accepted: 18 Aug 2017
Published online: 27 Jul 2018 *

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