Fuzzy Switch-Gain Sliding Mode Control for Robotic Manipulators Based on an Extended Kalman Filter

This paper presents a synthesis of an intelligent controller based on an enhanced version of sliding mode control (SMC) using a fuzzy logic system (FLS). FLS is designed to adjusting in an online manner the SMC switch-gain in order to achieve more acceptable performance for general nonlinear systems submitted to un-modeled dynamics and external disturbance. System states are assumed to be unknown and will be estimated by an extended Kalman filter (EKF) which is a stochastic and optimal state observer. The stability of the closed loop control system is proved using direct Lyapunov stability analysis method. The performance of the proposed controller is confirmed by extensive numerical simulations on Matlab platform. Simulation results applied on a two-link robot manipulator show that our algorithm outperforms the conventional SMC and demonstrates its superiority in terms of chattering phenomenon, un-modeled dynamic and external disturbance rejection.