Nonsingular terminal neural network sliding mode control for multi-link robots based on backstepping

Abstract

A new method of nonsingular terminal neural network sliding control based on backstepping for tracking control of multi-link robot manipulators is introduced in this paper. The proposed scheme combines the advantages of the adaptive control, neural network and sliding mode control strategies without precise system model information. It has on-line learning ability to deal with the parametric uncertainty and disturbances by adjusting the control parameters. A neural network sliding mode controller is designed via the Lyapunov stability theory in order to guarantee the high quality of the controlled system. The simulation results show that this method is feasible and effective.