Distributed coordination of multi-agent systems (MASs) has been widely studied in various emerging engineering applications, including connected vehicles, wireless networks, smart grids, and cyber-physical systems. In these contexts, agents make the decision locally, relying on the interaction with their immediate neighbors over the connected communication networks. The study of distributed coordination for the multi-agent system (MAS) with constraints is significant yet challenging, especially in terms of ubiquitous uncertainties, the heavy communication burden, and communication delays, to name a few. Hence, it is desirable to develop distributed algorithms for the constrained MAS with these practical issues. In this dissertation, we develop the theoretical results on robust distributed model predictive control (DMPC) algorithms for two types of control problems (i.e., formation stabilization problem and consensus problem) of the constrained and uncertain MAS and apply robust DMPC algorithms in applications of cooperative marine vehicles. More precisely, Chapter 1 provides a systematic literature review, where the state-of-the-art DMPC for formation stabilization and consensus, robust MPC, and MPC for motion control of marine vehicles are introduced. Chapter 2 introduces some notations, necessary definitions, and some preliminaries. In Chapter 3, we study the formation stabilization problem of the nonlinear constrained MAS with un- certainties and bounded time-varying communication delays. We develop a min-max DMPC algorithm with the self-triggered mechanism, which significantly reduces the communication burden while ensuring closed-loop stability and robustness. Chapter 4 investigates the consensus problem of the general linear MAS with input constraints and bounded time-varying delays. We design a robust DMPC-based consensus protocol that integrates a predesigned consensus protocol with online DMPC optimization techniques. Under mild technical assumptions, the estimation errors propagated over prediction due to delay-induced inaccurate neighboring information are proved bounded, based on which a robust DMPC strategy is deliberately designed to achieve robust consensus while satisfying control input constraints. Chapter 5 proposes a Lyapunov-based DMPC approach for the formation tracking control problem of co-operative autonomous underwater vehicles (AUVs) subject to environmental disturbances. A stability constraint leveraging the extended state observer-based auxiliary control law and the associated Lyapunov function is incorporated into the optimization problem to enforce the stability and enhance formation tracking performance. A collision-avoidance cost is designed and employed in the DMPC optimization problem to further guarantee the safety of AUVs. Chapter 6 presents a tube-based DMPC approach for the platoon control problem of a group of heterogeneous autonomous surface vehicles (ASVs) with input constraints and disturbances. In particular, a coupled inter-vehicle safety constraint is added to the DMPC optimization problem; it ensures that neighboring ASVs maintain the safe distance and avoid inter-vehicle collision. Finally, we summarize the main results of this dissertation and discuss some potential directions for future research in Chapter 7. / Graduate / 2023-04-19

This document details the design, implementation, testing, and analysis of an inverted short baseline acoustic positioning system. The system presented here is an above-water, air-based prototype for an underwater acoustic positioning system; it is designed to determine the position of remotely-operated underwater vehicles (ROVs) and autonomous underwater vehicles (AUVs) in the global frame using a method that does not drift over time. A ground-truth positioning system is constructed using a stacked hexapod platform actuator, which mimics the motion of an AUV and provides the true position of an ultrasonic microphone array. An ultrasonic transmitter sends a pulse of sound towards the array; microphones on the array record the pulse of sound and use the time shift between the microphone signals to determine the position of the transmitter relative to the receiver array. The orientation of the array, which is necessary to transform the position estimate to the global frame, is calculated using a Madgwick filter and data from a MEMS IMU. Additionally, a dead reckoning change-in-position estimate is formed using the IMU data. The acoustic position estimate is combined with the dead reckoning estimate using a Kalman filter. The accuracy of this filtered position estimate was verified to 22.1mm within a range of 3.88m in this air-based implementation. The ground-truth positioning system runs on an ESP32 microcontroller using code written in C++, and the acoustic positioning system runs on two STM32 microcontrollers using code written in C. Extrapolation of these results to the underwater regime, as well as recommendations for improving upon this work, are included at the end of the document. All code written for this thesis is available on GitHub and is open-source and well-documented.

<div>Exploring and surveilling the marine environment away from shore is critical for scientific, economic, and military purposes as we progress through the 21st century. Until recently, these missions far from shore were only possible using manned surface vehicles. Over the past decade, advances in energy density, actuators, electronics, and controls have enabled great improvements in vehicle endurance, yet, no solution is capable of supporting persistent operation especially when considering power hungry scientific surveys. This dissertation summarizes contributions related to the development of an adaptable underwater docking station and associated navigation solutions to allow applications in the wide range of maritime missions. The adaptable docking system is a novel approach to the standard funnel shaped docking station design that enables the dock to be collapsible, portable, and support a wide range of vehicles. It has been optimized and tested extensively in simulation. Field experiments in both pool and open water validate the simulation results. The associated control strategies for approach and terminal homing are also introduced and studied in simulation and field trials. These strategies are computationally efficient and enable operation in a variety of scenarios and conditions. Combined, the adaptable docking system and associated navigation strategies can form a baseline for future extended endurance missions away from manned support.</div><p></p>

<p dir="ltr">The growing movement toward sustainable use of ocean resources is driven by the pressing need to alleviate environmental and human stressors on the planet and its oceans. From monitoring the food web to supporting sustainable fisheries and observing environmental shifts to protect against the effects of climate change, ocean observations significantly impact the Blue Economy. Acknowledging the critical role of Autonomous Underwater Vehicles (AUVs) in achieving persistent ocean exploration, this research addresses challenges focusing on the limited energy and storage capacity of AUVs, introducing a comprehensive underwater docking solution with a specific emphasis on enhancing the terminal homing phase through innovative vision algorithms leveraging neural networks.</p><p dir="ltr">The primary goal of this work is to establish a docking procedure that is failure-tolerant, scalable, and systematically validated across diverse environmental conditions. To fulfill this objective, a robust dock detection mechanism has been developed that ensures the resilience of the docking procedure through \comment{an} improved detection in different challenging environmental conditions. Additionally, the study addresses the prevalent issue of data sparsity in the marine domain by artificially generating data using CycleGAN and Artistic Style Transfer. These approaches effectively provide sufficient data for the docking detection algorithm, improving the localization of the docking station.</p><p dir="ltr">Furthermore, this work introduces methods to compress the learned docking detection model without compromising performance, enhancing the efficiency of the overall system. Alongside these advancements, a station-keeping algorithm is presented, enabling the mobile docking station to maintain position and heading while awaiting the arrival of the AUV. To leverage the sensors onboard and to take advantage of the computational resources to their fullest extent, this research has demonstrated the feasibility of simultaneously learning docking detection and marine wildlife classification through multi-task and transfer learning. This multifaceted approach not only tackles the limitations of AUVs' energy and storage capacity but also contributes to the robustness, scalability, and systematic validation of underwater docking procedures, aligning with the broader goals of sustainable ocean exploration and the blue economy.</p>