Adaptive fuzzy logic position control of a Stepper motor with Extended Kalman Filter

The present paper proposes an adaptive fuzzy logic control (AFLC) for the position control of a Stepper motor. The weights used for combining the fuzzy rules are also updated, using the least mean square algorithm. The paper also demonstrates a Kalman filter for the estimation of motor parameters like speed and flux vector position. The estimation is ensured to be robust even in the presence of arbitrary fluctuation of the input line currents. The modified 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 the state space 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.