Remedial neural network inverse control of a multi-phase fault-tolerant permanent-magnet motor drive for electric vehicles
by Duo Zhang; Guohai Liu; Wenxiang Zhao
International Journal of Vehicle Autonomous Systems (IJVAS), Vol. 11, No. 2/3, 2013

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.

Online publication date: Tue, 30-Sep-2014

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