Adaptive LOS Path Following for a Podded Propulsion Unmanned Surface Vehicle with Uncertainty of Model and Actuator Saturation

Abstract

This paper addresses three related issues concerning the path following control of a podded propulsion unmanned surface vehicle (USV), namely modeling, guidance and control. The pod is different from the general propeller-rudder propulsion device, and its essence is a vector thruster. Therefore, first, through various assumptions and simplification, the three-degree of freedom (DOFs) planar motion model of the podded propulsion USV is established. Then, the classical line-of-sight (LOS) guidance strategy is improved by adaptive sideslip angle and a time-varying lookahead distance. Based on the guidance system, the corresponding controllers for yaw rate and surge speed are presented, which are combined by backstepping technology, the neural network minimum parameter learning method and the neural shunting model. Specifically, the neural network minimum parameter learning method is proposed to compensate the uncertainty of the model and the immeasurability of external disturbances, and the neural shunting model is employed to cope with the “explosion of complexity” problem of backstepping. Meanwhile, an auxiliary dynamic system is introduced to prevent actuator saturation (input saturation). All error signals of the system are proven to be uniformly ultimately bounded (UUB) by employing Lyapunov stability theory. Finally, two numerical simulations are given to prove the correctness of the proposed scheme.