LAUSR

Abstract In this manuscript, an efficient hybrid trajectory tracking control scheme has been proposed for an autonomous underwater vehicle in the presence of structured and unstructured uncertainties. To cope with these uncertainties, the model-dependent control scheme is successfully combined with the model-free control scheme. Due to the effects of the uncertainties, the full knowledge of the dynamic model of the vehicle cannot be accurately obtained in real applications. Therefore whatever the partial information is available about the dynamics of the system has been utilized in the controller design. A radial basis function neural network is utilized for the approximation of the unknown dynamics without requiring offline learning. To compensate for the unknown effects like the external disturbances and the reconstruction error of the neural network, an adaptive compensator is also added to the part of the controller. For the stability analysis, the online learning of the parameters and the neural network weights are used in the Lyapunov approach. Based on the Lyapunov stability criteria and Barbalat lemma, the tracking errors converge to zero asymptotically. Finally, comparative numerical simulations are performed over a four-degree of freedom autonomous underwater vehicle and efficiency and applicability of the proposed control framework is validated in a comparative manner with the existing controllers.