Adaptive multivariable super-twisting algorithm for trajectory tracking of AUV under unknown disturbances

Autonomous underwater vehicles (AUV) have been widely used in underwater missions. The motion model of AUV is affected by factors such as parameter uncertainty and disturbances from ocean environment. How to accurately track trajectories under unknown disturbances is a crucial issue. In this paper, an adaptive multivariable super-twisting algorithm (AMSTA) with a nonlinear extended state observer (NLESO) is developed for autonomous underwater vehicles (AUV) to reduce the trajectory tracking error and address the problem of unknown disturbance. First, a novel finite-time extended state observer is designed to estimate and compensate the uncertain nonlinear disturbance. Second, this research presents an improved adaptive multivariable super-twisting algorithm via Lyapunov theory to address the trajectory tracking problem. Finally, simulation results demonstrated the effectiveness and superiority of the proposed scheme.