Trajectory Tracking of Two-Joint Space Robot using Wavelet Neural Networks and Sliding Mode Control

Abstract

In this paper, the combination of wavelet neural networks (WNN) and sliding mode control (SMC) is proposed and simulated to solve the problem of trajectory-tracking control of a two-link robot manipulator with periodic interference. The difficulties of designing control algorithm are mainly focused on achieving accurate trajectory tracking and good control performance with the guarantee of stability and robustness under uncertain cyclical interference. In order to deal with these issues, WNN is used to approximate the functions of control object and unknown periodic disturbance. In this three-layer neural networks design, a widely used Mexican hat wavelet as an activation function has been applied for hidden-layer neurons. Combined with the SMC theory, the adaptive learning laws of networks parameters are derived in the sense of Lyapunov stability analysis so that the tracking error and convergence of the weight can be guaranteed in this control system. The better effectiveness of proposed SMC and WNN control algorithm is demonstrated by numerical simulation on a two-link robot manipulator, as comparing with that of Gauss Radial Basis Function (GRBF) neural networks.