Ultra-precision positioning using adaptive fuzzy-Kalman filter observer

In this paper, we design a hybrid controller based on adaptive fuzzy-Kalman filter observer. With external disturbances, it is hard to predict the measurement noise precisely under time varying environments. In order to improve the precision of the measuring instrument, the measurement noise covariance is updated so as to minimize the discrepancy which resides in the estimation and measurement by using Kalman filter observer and fuzzy control with covariance-matching technique. Then a new robust controller is presented by applying LQ-sliding mode control in the positioning system. Through some simulation results, the effectiveness of the proposed controller is proved. In spite of the applied disturbance signal, the LQ-sliding mode control based on fuzzy-Kalman filter observer maintains the stage position within a performance requirement and reduces the chattering effect.