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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.