Adaptive Tracking Control of Underactuated USV Based on Back-stepping and RBF Neural Network

Abstract

In order to solve the problem of trajectory tracking control of unmanned surface vehicles (USV) with unknown speed information, an adaptive control algorithm based on Radial Basis Function (RBF) neural network and back-stepping method is proposed. This algorithm uses the back-stepping method to design an easy-to-implement control input based on the model parameters, uses the high-gain observer to estimate the speed information, and uses the RBF neural network to estimate the model parametric uncertainties and the environmental disturbances such as wind and wave. Then the control law and the weight update law of RBF neural network are designed. Finally, the systemic stability is proved by Lyapunov function. Simulational experiments and physical experiments verify the feasibility and effectiveness of this algorithm.