Intelligent complementary sliding mode fault tolerant control for six-phase motor drive system using TSK type FNN

An intelligent complementary sliding mode control (ICSMC) is proposed in this study for the fault tolerant control of a six-phase permanent magnet synchronous motor (PMSM) drive system with open phases. First, the dynamics of the six-phase PMSM drive system with a lumped uncertainty is described in detail. Then, the fault detection and operating decision method are briefly introduced. Moreover, to improve the required control performance and to maintain the stability of the six-phase PMSM drive system under faulty condition, the ICSMC is developed. In this approach, a TSK type fuzzy neural network with asymmetric membership function (TSKFNN-AMF) estimator with accurate approximation capability is employed to estimate the lumped uncertainty. Furthermore, the adaptive learning algorithms for the online training of the TSKFNN-AMF are derived using the Lyapunov theorem to guarantee the closed-loop stability. In addition, 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 of the proposed fault tolerant control system. Finally, some experimental results are illustrated to demonstrate the validity of the proposed fault tolerant control for the six-phase PMSM drive system via ICSMC.