MFALC Control of Aquaculture Vessel Based on Improved ILOS Guidance Law

To mitigate the impact of lateral deviation in the trajectory control of aquaculture vessels, an improve ILOS guidance law is proposed. Addressing complex sea conditions, unstable weather scenarios, and uncertainties in load, a model-free adaptive learning control (MFALC) method for aquaculture vessel heading control is introduced. Firstly, by incorporating the actual vessel position, desired trajectory, and the improved ILOS guidance law, the desired heading is obtained, and a dynamic linearization model is established solely based on input-output data. Secondly, a Pseudo-Partial Derivative (PPD) estimation algorithm is presented. Subsequently, the MFALC control is designed in conjunction with the PPD algorithm. Finally, the superiority of the proposed control method over traditional PID control is validated through simulations. Simulation results demonstrate that the proposed control method enables rapid and stable trajectory control for aquaculture vessels.