Data Driven Identification and Model Predictive Control for a Catamaran Surface Vessel

Abstract Autonomous Surface Vehicles (ASVs) have received increasing attention in various maritime applications over the years. Their ability to operate autonomously in complex and confined environments, such as narrow inland waterways, presents a unique set of opportunities and challenges. This paper presents a comprehensive study on the dynamics identification and subsequent development of a Model Predictive Controller (MPC) for a catamaran surface vessel. The catamaran surface vessel under investigation was fabricated in-house with a holonomic thruster configuration and a comprehensive sensor suite. This study focuses on experimental identification of a 3 degree of freedom maneuvering model of the fully actuated surface vessel. With a well-defined dynamic model in place MPC is designed for efficiently tracking a path while maintaing surge velocity. The MPC framework is chosen due to its ability to optimize control inputs over a finite prediction horizon, ensuring the catamaran follows desired trajectories while adhering to operational constraints and actuator constraints. This paper outlines a holistic study on the dynamics identification and MPC development of an ASV to impact a wide range of applications, from oceanographic research to coastal security, by providing a robust control framework that can navigate the challenges of dynamic marine environments.