Trajectory Linearization-Based Adaptive PLOS Path Following Control for Unmanned Surface Vehicle with Unknown Dynamics and Rudder Saturation

Abstract

This paper presents a novel robust control strategy for path following of an unmanned surface vehicle (USV) suffering from unknown dynamics and rudder saturation. The trajectory linearization control (TLC) method augmented by the neural network, linear extended state observer (LESO), and auxiliary system is used as the main control framework. The salient features of the presented strategy are as follows: in the guidance loop, a fuzzy predictor line-of-sight (FPLOS) guidance law is proposed to ensure that the USV effectively follows the given path, where the fuzzy method is introduced to adjust lookahead distance online, and thereby achieving convergence performance; in the control loop, we develop a practical robust path following controller based on enhanced TLC, in which the neural network and LESO are adopted to handle unmodeled dynamics and external disturbances, respectively. Meanwhile, a nonlinear tracking differentiator (NTD) is constructed to achieve satisfactory differential and filter performance. Then, the auxiliary system is incorporated into the controller design to handle rudder saturation. Using Lyapunov stability theory, the entire system is ensured to be uniformly ultimately bounded (UUB). Simulation comparisons illustrate the effectiveness and superiority of the proposed strategy.

Keywords

• path following
• TLC method
• LESO
• predictor line-of-sight
• fuzzy method
• neural network