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Trajectory Tracking Control of an Underactuated AUV Based on Backstepping Sliding Mode With State Prediction

This paper studies the trajectory tracking problem of an underactuated autonomous underwater vehicle (AUV) under the uncertainty of model parameters and input delay. Different from previous algorithms, a novel control algorithm is proposed which is a combination of RBF neural network algorithm and state prediction using backstepping sliding mode control method. The RBF neural network algorithm is used to estimate the composite interferences of model parameter uncertainties and external disturbances. Meanwhile, an appropriate virtual control law is designed for the horizontal trajectory tracking of the underactuated AUV by backstepping technique. Further, the longitudinal and heading control laws are proposed using the nonsingular fast terminal sliding mode method. Then, it is proved that the AUV velocity tracking error can converge to zero in a finite time by the Lyapunov theory. Finally, the effectiveness and robustness of the approach is illustrated by the simulation results.