Adaptive Course Control-Based Trajectory Linearization Control for Uncertain Unmanned Surface Vehicle Under Rudder Saturation

In the presence of the uncertain system dynamics, unknown time-varying disturbances and rudder saturation, this paper develops a novel robust adaptive course control scheme for unmanned surface vehicle (USV). Considering the characteristics of the rudder servo system, a double loop course controller of the practical and concise is proposed by the enhanced trajectory linearization control (TLC) technology. The key features of the developed controller are that, first, the neural networks are employed to online approximate unmodeled dynamics, and adaptive techniques are adopted to deal with completely unknown external disturbances; second, auxiliary systems that are governed by smooth switching functions, are developed in an unprecedented manner to compensate for the saturation constraints on actuators. The main innovation can be summarized as that the TLC technology is applied to the USV motion control field as a new control algorithm, and the enhanced technology based on traditional TLC not only reduces the number of adjustment parameters but also has simple structure and high robustness. Furthermore, a low frequency learning method improves the applicability of the algorithm. The stability analysis is established using the Lyapunov theory. Simulation results and comparison verify the effectiveness of the proposed strategy.