GVF-based guidance and super-twisting control of autonomous surface vehicle for target tracking in obstacle environments with experiments

Abstract

This paper addresses the target tracking problem of an under-actuated autonomous surface vehicle (ASV) subject to internal uncertainties and exogenous forces in ocean environments with static and dynamic obstacles. A collision-free target tracking (CFTT) control method is proposed based on a guiding vector field (GVF) guidance and a super-twisting algorithm (STA) control. Specifically, a resultant GVF is developed to guide the ASV towards the target position and to avoid multiple obstacles. Based on the resultant GVF, a saturated guidance law is designed to calculate the surge velocity and angular velocity commands. Then, robust exact differentiator (RED)-based observers are developed to estimate the lumped disturbances within finite time. By using the estimated lumped disturbances, STA-based anti-disturbance kinetic control laws are designed to track the given velocity commands within finite time. The finite-time input-to-state stability of the closed-loop system is proven based on cascade theory. Emulational and experimental validations are conducted to substantiate the efficacy of the proposed CFTT control method.