Abstract
In this paper, a radial basis function (RBF) neural network based sliding mode control is presented. Taking CW equation as dynamics model, the parameter perturbation and external disturbance has been considered. The controller consists of two parts, one is the feed-forward control, and the other is feedback sliding mode control using RBF neural network. Based on Lyapunov stability theory, the adaptive law of neural network has been obtained. Simulation results suggest that the control scheme can initialize the formation and maintain the relative motion according to the desired trajectory. The inherent chattering of sliding mode control has been weaken and the accuracy of trajectory tracking has greatly improved with the use of RBF neural network.
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