Heuristic Strategy-Empowered Real-Time Path Following for Autonomous Surface Vessel With Adaptive Line-of-Sight Guidance

In extreme environments, such as polar oceans, where potential hazards like sea ice are prevalent, deploying autonomous surface vessel (ASV) can enhance operational efficiency and safeguard personnel. As these extreme environments necessitate higher performance standards, particularly in terms of path-following accuracy and control stability, we introduce in this research an ASV path-following control method predicated on an enhanced proportional–integral–derivative (PID) parameters tuning algorithm aimed at reducing path-following errors and bolstering control stability. First, the adaptive line-of-sight (ALOS) guidance algorithm is devised to determine the desired ASV heading by designing the forward-looking range adjustment strategy. Second, the improved sparrow search algorithm (ISSA) is proposed for PID parameters tuning. Since the lack of stability of the standard Sparrow Search Algorithm (SSA), the producer update strategy is modified, and the Brown–Levy mutation strategy is designed to improve the global search ability of the algorithm. Finally, the virtual ASV simulation platform is built, and the real-time PID controller is constructed by designing the PID real-time tuning strategy. The parameters of the Nomoto ship motion model are fitted in the simulation platform according to different marine environments, and the PID controller parameters are updated in real-time by ISSA to improve the path following accuracy. Experimental results of the marine environment simulation test and the real-world experiment show that the ALOS guidance algorithm can effectively generate the current desired rudder angle. The PID controller based on ISSA has the best performance in computer simulation. The average overshoot is 2.79%, and the average convergence time is 20.1 s. In the real-world experiment, the average path following error of ISSA Real-Time is reduced by 51.0% compared with that of SSA and 27.2% compared with that of ISSA. The improved control method can better satisfy the control requirements of the ASV, enhance control stability, and achieve more precise path following.