Composite learning tracking control for underactuated autonomous underwater vehicle with unknown dynamics and disturbances in three-dimension space

In this paper, a composite learning tracking control scheme is developed for underactuated autonomous underwater vehicles (AUVs) in the presence of unknown dynamics and time-varying disturbances. Line-of-sight (LOS) tracking control is employed to handle the underactuation of AUVs. The unknown dynamics of the AUVs are approximated by adaptive neural networks (NNs). The serial-parallel estimation models are built to obtain the prediction errors. Both the prediction errors and the tracking errors are employed to design the composite weights updating law. Nonlinear disturbance observers based on composite learning control are constructed to estimate time-varying disturbances. The stability analysis via the Lyapunov method indicates that the uniformly ultimate boundedness of all signals of the AUV trajectory tracking close-loop control system. The simulation results on an AUV verify the effectiveness and the superiority of the proposed composite learning tracking control scheme.

Automated summary

This paper develops a composite learning tracking control scheme for underactuated autonomous underwater vehicles (AUVs) in the presence of unknown dynamics and time-varying disturbances. Line-of-sight (LOS) tracking control and adaptive neural networks are employed to handle the underactuation and approximate the unknown dynamics of the AUVs. Stability analysis via the Lyapunov method is conducted, and nonlinear disturbance observers are constructed to estimate time-varying disturbances, verifying the effectiveness and superiority of the proposed control scheme through simulation results on an AUV.