Iterative Learning Control for Path-Following of ASV With the Ice Floes Auto-Select Avoidance Mechanism

The autonomous and security are the crucial requirements in fields of the polar transportation. This paper proposes a newly iterative learning control framework for the autonomous surface vessels (ASV) to implement the path-following operation in the ice floes scenario. The proposed framework is divided into two parts: the guidance and control. For the former, the ice floes are firstly identified into threatening and non-threatening based on the size. Subsequently, the obstacle area of each threatening ice floe is programmed considering the underwater portion. Then the ice floes avoidance guidance with auto-select mechanism for ice-zone traversal mission is constructed by setting the hazard threshold and target point. For the latter, a robust adaptive iterative learning control (ILC) system is designed for the path-following mission, where the control accuracy increases with the number of iterations. The stability of the closed-loop control system is proved with utilization of the Lyapunov theorem. Finally, two numerical examples are provided to evaluate the advantages and accuracy of the proposed algorithm, where the ice floes are generated with irregular.