Adaptive output-feedback control for stochastic robot system based on neural network

Abstract

This paper investigates the output-feedback control problem for a class of robot system with stochastic disturbances and a single-link manipulator. By utilizing a novel neural network (NN) approximation approach, the adaptive parameter is only one and the nonlinear terms are successfully handled without growth conditions. The constructed adaptive output-feedback controller guarantees the closed-loop robot system to be semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the controller is validated by simulating the robot system.