Title: Remedial neural network inverse control of a multi-phase fault-tolerant permanent-magnet motor drive for electric vehicles

Authors: Duo Zhang; Guohai Liu; Wenxiang Zhao

Addresses: School of Electrical and Information Engineering, Jiangsu University, No. 301, Xuefu Road, Zhenjiang 212013, China ' School of Electrical and Information Engineering, Jiangsu University, No. 301, Xuefu Road, Zhenjiang 212013, China ' School of Electrical and Information Engineering, Jiangsu University, No. 301, Xuefu Road, Zhenjiang 212013, China

Abstract: A five-phase in-wheel fault-tolerant interior permanent-magnet (FT-IPM) motor incorporates the merits of high efficiency, high power density and high reliability, suitable for Electric Vehicles (EVs). A new remedial Neural Networks Inverse (NNI) control strategy is proposed to attain the post-fault operation. In this scheme, the NN is used to approximate the inverse model of the FT-IPM motor. With this NNI system and the original motor drive combined, a pseudo-linear compound system can be obtained. The simulation demonstrates that the proposed control strategy leads to excellent control performance at the faulty mode and offers good robustness against load disturbance.

Keywords: fault tolerance; interior permanent magnet motors; remedial control; neural networks; inverse control; electric vehicles; inverse modelling; motor drives; simulation; control performance; load disturbance.

DOI: 10.1504/IJVAS.2013.053783

International Journal of Vehicle Autonomous Systems, 2013 Vol.11 No.2/3, pp.279 - 291

Received: 16 Sep 2011
Accepted: 13 Dec 2011

Published online: 09 May 2013 *

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