This paper presents a sliding-mode observer-based model predictive control (SMO-MPC) strategy for image-based visual servoing (IBVS) of fully-actuated underwater vehicles subject to field of view and actuator constraints and model uncertainties. In the proposed SMO-MPC controller, the visual system model and the approximate underwater vehicle model are used to predict the future trajectories from the current states driven by input candidates over a certain horizon. With the consideration of system uncertainties, including external disturbances and unknown dynamic parameters, a sliding-mode observer is designed to estimate the modeling mismatch, which is feedforward to the dynamic model in MPC. The actual control signals are generated at each step by minimizing a cost function of predicted trajectories under system constraints. The effectiveness of the proposed SMO-MPC IBVS controller is verified by comparative simulations using a fully-actuated underwater vehicle with different control configurations. INDEX TERMS Underwater vehicles, image-based visual servo control, model predictive control, sliding mode disturbance observer.

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Cooperative localization (CL) is considered a promising method for underwater localization with respect to multiple autonomous underwater vehicles (multi-AUVs). In this paper, we proposed a CL algorithm based on information entropy theory and the probability hypothesis density (PHD) filter, aiming to enhance the global localization accuracy of the follower. In the proposed framework, the follower carries lower cost navigation systems, whereas the leaders carry better ones. Meanwhile, the leaders acquire the followers’ observations, including both measurements and clutter. Then, the PHD filters are utilized on the leaders and the results are communicated to the followers. The followers then perform weighted summation based on all received messages and obtain a final positioning result. Based on the information entropy theory and the PHD filter, the follower is able to acquire a precise knowledge of its position.