Intelligent Fault Tolerant Control of Six-Phase Motor Drive System

A Takagi-Sugeno-Kang type fuzzy neural network with asymmetric membership function (TSKFNN-AMF) is proposed in this study for the fault tolerant control of six-phase permanent magnet synchronous motor (PMSM) drive system. First, the dynamics of six-phase PMSM drive system, the fault detection and operating decision method are briefly introduced. Then, to achieve the required control performance and to maintain the stability of six-phase PMSM drive system under faulty condition, the TSKFNN-AMF control, which combines the advantages of TSK type fuzzy logic system (FLS) and AMF, is developed. The network structure and online learning algorithm of the TSKFNN-AMF are described in detail. Moreover, to enhance the control performance of the proposed intelligent fault tolerant control, a 32-bit floating-point digital signal processor (DSP) TMS320F28335, is adopted for the implementation. Finally, some experimental results are illustrated to show the validity of the proposed intelligent fault tolerant control for the six-phase PMSM drive system.