Tracking control of robot manipulators via orthogonal polynomials neural network

Abstract

In this paper, an orthogonal functions neural network is used to achieve the control of nonlinear systems. The adaptive controller is constructed by using Chebyshev orthogonal polynomials neural network, which has advantages such as simple structure and fast convergence speed. The adaptive learning law of orthogonal neural network is derived to guarantee that the adaptive weight errors and tracking errors are bound by using Lyapunov stability theory. Simulation results are given for a two-link robot in the end of the paper, and the control scheme is validated. © 2009 Springer Berlin Heidelberg.