Neural adaptive control of two-link manipulator with sliding mode compensation

Abstract

We develop a new neurocontroller for robot manipulators. A simple dynamic neural network is used to estimate the unknown robot manipulators, then the direct linearization controller is derived via this neuro-identifier. Since the approximation capability is limited, another robust sliding mode compensator is addressed. In this paper we give a bound for the identification error of the parallel neuro-identifier, and establish a bound for the tracking error of the hybrid controller.