Robust adaptive sliding mode tracking control using an RBF neural network for robotic manipulators

Abstract

A new robust adaptive sliding mode tracking control scheme using an RBF neural network is proposed for rigid robotic manipulators to achieve robustness and asymptotic error convergence. A key feature of this scheme is that the prior knowledge of the upper bound of the system uncertainties is not required. An adaptive RBF neural network is used to learn the upper bound of system uncertainties. The output of the neural network is then used as a compensator parameter in the sense that the effects of the system uncertainties can be eliminated and asymptotic error convergence can be obtained for the closed loop robotic control system.