The underwater vehicle-manipulator (UVM) interaction tasks require that UVM can adapt to different environments and have good stability properties. However, the complicated structures of UVM and uncertainties of the environment lead to poor interaction. In addition, transient performance related to overshoot and convergence is required during the interaction to ensure the safety of the UVM and environment. In this paper, an inner-loop/outer-loop control formulation is developed for underwater vehicle-manipulator interacting with environment, subject to unknown system and environment dynamic. For the inner-loop, an approximation-free state feedback controller is designed for UVM system without a prior knowledge of dynamics, capable of guaranteeing trajectory tracking with prescribed performance. A prescribed function is constructed, which allows the convergence time and convergence accuracy to be predetermined. Moreover, it is independent of the initial conditions. For the outer-loop, an off-policy reinforcement learning algorithm is presented to optimize the parameters of the admittance model without requiring the information of environment dynamic. The proposed admittance optimization is feasible for arbitrary desired trajectory. Finally, simulation and experimental results validate the effectiveness of the proposed model-free tracking controller.
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