Sliding mode controller based on fuzzy neural network optimization for direct torque controlled PMSM

A sliding mode controller for permanent magnet synchronous machine (PMSM) is investigated in this paper, in which direct torque control (DTC) concept, variable structure control and space vector modulation (SVM) are integrated to achieve high performance. It features in very low flux and torque ripple, strong robustness and fixed switching frequency. Then, an FNN is investigated to optimize the control gain matrix of sliding mode controller. The inputs of the FNN are the switching surface and its derivative, and the output of the FNN is the estimated bound of uncertainties. The theoretical analyses for the proposed FNN sliding-mode controller are described in detail. Simulation results show that the proposed FNN sliding-mode controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations. Furthermore, comparing with the sliding-mode controller, the chattering phenomenon is much reduced by the proposed FNN sliding-mode controller.