Adaptive Gaussian membership function fuzzy logic vector control of permanent magnet synchronous motor with a robust Extended Kalman Filter

This paper proposes an adaptive fuzzy logic control (FLC) for the speed control of the permanent magnet synchronous motors (PMSM). The mean and the variance of the Gaussian membership function are updated in real time, until convergence of the error is achieved. The weights used for combining the fuzzy rules are also updated, using the least mean square algorithm. The paper also demonstrates a robust Kalman filter for the estimation of motor quantities. The estimation is ensured to be robust even in the presence of arbitrary fluctuation of the input line currents. The modified robust Extended Kalman Filter (EKF) rejects the outliers in real-time, thereby eliminating the need for manual intervention in tuning the parameters of the EKF. The stability of the controller is checked by computing the Lyapunov exponent from the evolution of direct axis current, quadrature axis current, synchronous speed and found to be stable at all the time. The simulation results show that the proposed control strategy operates robustly under modeling uncertainty, with a good dynamic performance.