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.