ADAPTIVE SLIDING MODE CONTROL FOR TWIN ROTOR MIMO SYSTEM USING RADIAL BASIS FUNCTION NEURAL NETWORK

Abstract

This paper presents a adaptive sliding mode control method using radial basis function neural network (RBFNN) to control a yaw and pitch angle of a twin rotor multil input - multil output system (TRMS). This controller has the advantage of being able to learn and approximate unknown nonlinear functions with arbitrary precision regardless of the various system parameters while the tranditional sliding controller need to accurately calculate the nonlinear functions so the chatering occurs under the affect of the uncertain system parameters and disturbance. The adaptive controller using RBFNN will update the online neural network weights so that the output vectors of neural network are trained online to approximate uncertainty components of the system. The simulation results demonstrate the adaptive controller using RBFNN is capable tracking different reference trajectories in satisfactory manner.