Analysis and implementation of advanced control and estimation algorithms applied to a fleet of autonomous marine surface vehicles

Abstract

This doctoral thesis presents a comprehensive analysis, development, and experimental validation of advanced control and state estimation algorithms specifically tailored for coordinated operation of autonomous marine surface vehicles (ASVs). Motivated by the practical constraints of implementing robust navigation and control strategies on low-cost, underactuated catamaran-type vessels, this research addresses critical challenges associated with limited sensor instrumentation, environmental disturbances, and complex marine vehicle dynamics. Initially, a robust system identification methodology was developed and experimentally validated to accurately characterise the dynamic response of ASVs, capturing essential vessel dynamics from basic position and orientation sensors without requiring direct velocity and acceleration measurements. Both static and dynamic propulsion models were identified, yielding highly reliable and precise representations of the vessel’s motion across a broad operational range, thereby providing a solid foundation for subsequent estimation and control frameworks. Subsequently, the thesis explored advanced state estimation techniques, specifically extended state observers (ESOs), capable of accurately reconstructing unmeasured states and disturbances from inherently noisy measurements. Three distinct ESOs were rigorously evaluated experimentally, including a novel zonotopic observer integrated with agrey-boxinputgainidentificationmethod, demonstratingsuperior robustness and practical effectiveness in dealing with system uncertainties and varying operational conditions. Building upon these estimation methodologies, an enhanced zonotopic observer framework was further proposed, featuring real-time online input gain identification and direct integration of yaw rate sensor measurements. This approach delivered improved resilience against parameter variations and actuator degradation, markedly enhancing predictive performance and reliability under dynamic marine environments. The culmination of this research is the development of a hierarchical control architecture that integrates high-level flocking algorithms, mid-level path-following guidance, and low-level dynamic control into a unified operational framework. This innovative approach effectively manages fleet coordination, collision avoidance, and environmental disturbances, while explicitly handling actuator limitations. Extensive simulations and quasi-experimental validations using the CyberShip II and Yellowfish ASV platforms within ROS-based realistic marine environments have demonstrated cohesive fleet behaviour, significant reductions in navigation errors, and reliable performance under diverse conditions. Through the integration of robust modelling, adaptive estimation, and hierarchical control strategies, this thesis validates the central hypothesis that advanced f leet-level algorithms can be successfully implemented in affordable ASVs equipped with minimal sensor configurations. The methodologies and insights provided herein contribute substantially to the field, bridging critical gaps between theoretical developments and practical applications in autonomous maritime operations, thus laying the groundwork for future research and broader real-world deployment of coordinated autonomous marine vehicle fleets.