Guoqing Xia, Ang Zhao, Huiyong Wu, and Ju Liu
Surface ships, trajectory tracking, output feedback, RBF neural network, fuzzy logic system (FLS), input nonlinearities
In this paper, we consider the problem of trajectory tracking for fully actuated dynamic positioning (DP) ships in the presence of parametric uncertainties, unknown external disturbances and input nonlinearities. A linear dynamic compensator is designed to compensate the linear part in the system, while the parameters are obtained through the pole assignment technique. The robust neural adaptive control improves the robustness against the unknown nonlinear parts. As it is difficult to measure velocities of the ships accurately, a convenient observer is proposed. An auxiliary system and a fuzzy logic system (FLS) based dead-zone compensator are designed separately to cope with the saturation and unknown dead-zone characteristics of actuators, followed by an integration of these two compensation methods into the final control scheme. Through the Lyapunov’s direct method, the adaptive updating laws of both RBF weights and FLS parameters are derived, and the ultimate boundedness of the closed-loop signals is shown. Simulation results are demonstrative of the excellent performance of the proposed control scheme.
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