Robust path-following control based on trajectory linearization control for unmanned surface vehicle with uncertainty of model and actuator saturation

摘要

This article develops a novel path-following control strategy for underactuated unmanned surface vehicle (USV) subject to unmodeled dynamics and unknown multiple disturbance. A practical robust path-following controller is proposed using trajectory linearization control (TLC) technology, neural network, and auxiliary design system. First, the greatest advantage of this article is that the TLC technology is first introduced into the field of USV motion control, which provides a new direction for TLC technology research. Second, the underactuated model based on a transformation of the USV kinematics to Serret-Frenet frame is simplified by introducing a nonlinear coordinate transformation. Meanwhile, to improve the robustness and reduce the computational complexity, radial basis function neural network is replaced by neural network with minimum learning parameter method to compensate for unmodeled dynamics and unknown multiple disturbance. In addition, an auxiliary dynamic system is used to reduce the risk of actuator saturation. The stability of the whole system was proved based on the Lyapunov criteria. Finally, the comparison results demonstrate the superior performance of the proposed approach. (c) 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.