Adaptive state feedback control of robotic manipulators using neural networks

Abstract

In this paper, a neural network based adaptive state feedback control scheme is proposed to solve the problem of trajectory tracking in the joint space for robotic manipulators with the presence of high uncertainty in the system parameters. First nonlinear behavior of the robot is approximately eliminated by applying a linearizing control, the closed loop dynamics is stabilized using static compensator. Then the parametric uncertainty is parameterized and eliminated using a single hidden layer neural network. The adaptation law for the NN and the boundedness of the error signals are proved from Lyapunov theorem of stability. The performance of the proposed controller is validated on two-link manipulator and demonstrated through computer simulation.