EKF-Based Fuzzy Sliding Mode Control Using Neural Networks

This essay addresses an observer-based robust fuzzy sliding mode controller design to maintain stability and better tracking of a nonlinear system. To suppress the chattering phenomenon, a fuzzy sliding mode controller (sf) is used. Also, a neural network is utilized to generate gain of the optimal controller. The performance of the designed controller is evaluated with the Magnetic Levitation System (MLS), which has a sever nonlinearity in its model. Also, the Extended Kalman Filter (EKF) is utilized to approximate the states of this system accurately in the presence of perturbation and environmental noise. The simulation results are reported to show the effectiveness of the proposed method.