Enhancing Path Following of Underactuated Water-Cleaning ASVs Using Adaptive MPC: Real-Time Parameter Estimation and Sensitivity Analysis in Dynamic Marine Environments

Autonomous surface vehicles (ASVs) offer a scalable solution for marine debris collection, but their dynamics change as trash accumulates. This work explores adaptive control for underactuated water-cleaning ASVs using model-predictive control (MPC) with real-time parameter estimation via moving horizon estimation (MHE). While MHE effectively tracked linear drag variations, it required sufficient excitation for added mass estimation and was computationally expensive. We also analyzed the sensitivity of explicit hydrodynamic estimation to measurement noise and control inputs, revealing potential inaccuracies. A thorough sensitivity study on nominal MPC under varying hydrodynamic conditions, based on the assumption of simulated trash accumulation effects on system dynamics, motivated the development of an adaptive MPC controller, which showed better performance in extreme scenarios (e.g., heavy trash accumulation and strong currents). Field experiments further indicated that wind-induced currents had a greater impact on MPC performance than trash accumulation. In addition, we developed a comprehensive Python simulation platform to incorporate real-time disturbances, highlighting MPC’s limitations when the model is inaccurate. These findings underscore the need for robust control strategies in dynamic marine environments, paving the way for improved autonomy in water-cleaning ASVs.