Nonsingular terminal neural network sliding mode control for manipulator joint based on backstepping

A new method of nonsingular terminal neural network sliding control based on backstepping for tracking control of multi-link robot manipulators with modeling error and external disturbances is introduced. Nonsingular terminal sliding mode surface is used which has property of finite-time convergence. According to sliding mode control theory, backstepping is used to design nonsingular terminal sliding mode controller. To confirm the upper bound of uncertainties in sliding-mode control system, a proper radial basis function neural networks controller is designed to estimate uncertain upper boundary on line. The system stability is proved by Lyapunov principle simulation results verify that this method improves the performances of trajectory tracking, enhances the robustness to modeling error and external disturbances and reduces chattering. ©2012 Journal of Mechanical Engineering.