EMPC-Based Tracking Control and Energy-Optimal Path Planning for an ASV

This study explores the use of a robust Explicit Model Predictive Control (EMPC) scheme for an Autonomous Surface Vehicle (ASV). The aim is to manage computational complexity related to online optimization, particularly for fast-response ASVs which are equipped with micro hardware with limited computational capabilities. The optimization process is performed offline a priori to a given mission, and the proposed EMPC scheme is shown to ensure stability via Lyapunov analysis. The authors also investigate the way in which the resulting ASV trajectories, especially taking into account polynomial trajecto-ries, affect the total ASV control effort. This qualitative compari-son of trajectories and respective control efforts is the basis from which ASV control performance is analyzed. Numerical analysis confirms the effectiveness of the proposed EMPC-based control strategies. Additionally, it reveals how energy consumption varies with different polynomial trajectories of varying degrees.