Network Based Control Of Unmanned Marine Vehicles

This book presents a comprehensive analysis of stability, stabilization, and fault detection in networked control systems, with a focus on unmanned marine vehicles. It investigates the challenges of network-based control in areas like heading control, fault detection filter and controller design, dynamic positioning, and cooperative target tracking. Communication networks in control systems can induce delays and dropouts, so the book presents the importance of stability analysis, stabilize, and fault detection. To help readers gain a deeper understanding of these concepts, the book provides fundamental concepts and real-world examples. This book is a valuable resource for researchers and practitioners working in the field of network-based control for unmanned marine vehicles.

Unmanned marine vehicles (UMVs) include autonomous underwater vehicles, remotely operated vehicles, semi-submersibles and unmanned surface craft. Considerable importance is being placed on the design and development of such vehicles, as they provide cost-effective solutions to a number of littoral, coastal and offshore problems. This book highlights the advanced technology that is evolving to meet the challenges being posed in this exciting and growing area of research.

Robotic marine vessels can be used for a wide range of purposes, including defence, marine science, offshore energy and hydrographic surveys, and environmental surveys and protection. Such vessels need to meet a variety of criteria: they must be able to operate in salt water, and to communicate and be controlled over large distances, even when submerged or in inclement weather. Further challenges include 3D navigation of individual vehicles, groups or squadrons. This book covers the current state of research in navigation, modelling and control of marine autonomous vehicles, and deals with various related topics, including collision avoidance, communication, and a range of applications. It provides valuable insights for an audience of researchers, academics and postgraduate students interested in autonomous marine vessels, robotics, and electrical and automobile engineering.

Unmanned marine vehicles (UMVs) is a collective term which can be used to describe autonomous underwater vehicles, remotely operated vehicles, semisubmersibles, and unmanned surface craft. These days there is considerable interest being shown in UMVs by the military, civilian and scientific communities for undertaking designated missions whilst either operating autonomously and/or in cooperation with other types of vehicle. More and more worldwide importance is also being placed on the design and development of such vehicles as they are capable of providing cost effective solutions to a number of littoral, coastal and offshore problems. This new book on the subject will cover topics including Navigation guidance and control of the following unmanned marine vehicles: remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), ncluding gliders, autonomous surface vehicles (ASVs).

This book constitutes the proceedings of the 18th Chinese Intelligent Systems Conference, CISC 2022, which was held during October 15–16, 2022, in Beijing, China. The 178 papers in these proceedings were carefully reviewed and selected from 185 submissions. The papers deal with various topics in the field of intelligent systems and control, such as multi-agent systems, complex networks, intelligent robots, complex system theory and swarm behavior, event-triggered control and data-driven control, robust and adaptive control, big data and brain science, process control, intelligent sensor and detection technology, deep learning and learning control guidance, navigation and control of aerial vehicles.

This book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems. A concise description of classical reinforcement learning (RL), the basics of optimal control with dynamic programming and network control architectures, and a brief introduction to typical algorithms build the foundation for the remainder of the book. Extensive research on data-driven robust control for nonlinear systems with unknown dynamics and multi-player systems follows. Data-driven optimal control of networked single- and multi-player systems leads readers into the development of novel RL algorithms with increased learning efficiency. The book concludes with a treatment of how these RL algorithms can achieve optimal synchronization policies for multi-agent systems with unknown model parameters and how game RL can solve problems of optimal operation in various process industries. Illustrative numerical examples and complex process control applications emphasize the realistic usefulness of the algorithms discussed. The combination of practical algorithms, theoretical analysis and comprehensive examples presented in Reinforcement Learning will interest researchers and practitioners studying or using optimal and adaptive control, machine learning, artificial intelligence, and operations research, whether advancing the theory or applying it in mineral-process, chemical-process, power-supply or other industries.

Unmanned marine vehicles (UMVs) is a collective term commonly used to describe autonomous underwater vehicles, remotely operated vehicles, semi-submersibles, and unmanned surface craft. UMVs are heavily used in the military, civilian, and scientific communities for undertaking designated missions whilst either operating autonomously and/or in co-operation with other types of vehicles. Advanced marine vehicles are increasing their capabilities and the degree of autonomy more and more in order to perform more sophisticated maritime missions. Remotely operated vehicles are no longer cost-effective since they are limited by economic support costs, and the presence and skills of the human operator. Alternatively, autonomous surface and underwater vehicles have the potential to operate with greatly reduced overhead costs and level of operator intervention. An unmanned aerial vehicle (UAV), commonly known as a drone, is an aircraft without a human pilot aboard. UAVs are a component of an unmanned aircraft system (UAS); these include a UAV, a ground-based controller, and a system of communications between the two. Compared to manned aircraft, UAVs were originally used for missions too "dull, dirty or dangerous" for humans. While they originated mostly in military applications, their use is rapidly expanding to commercial, scientific, recreational, agricultural, and other applications such as policing, peacekeeping and surveillance, product deliveries, aerial photography, agriculture, smuggling, and drone racing. Civilian UAVs now vastly outnumber military UAVs, with estimates of over a million sold by 2015, so they can be seen as an early commercial application of Autonomous Things, to be followed by the autonomous car and home robots. Nowadays, UMVs and UAVs are playing an increasingly important role in both controlling community and engineering applications. For example, UMVs and UAVs provide more efficient ways to execute various challenging tasks. However, these systems are usually featured with dynamics coupling, actuator saturation, underactuated structure, time-varying disturbance, etc., thereby resulting in great challenges and difficulties in system analysis and controller design. Recently, by employing intelligent approaches, advanced control methodologies for unmanned systems have been rapidly developed. Note that the dynamic environment is usually changing and the unmanned systems must adapt themselves accordingly. In this context, on one hand, more efforts should be focused on the methodology of the learning system. For example, fast adaptation and self-organizing capability are essentially required. On the other hand, advanced analysis tools should be deployed to enhance the control performance. Towards this end, human-like intelligence should be integrated tightly with nonlinear design for complex control tasks of autonomous systems. The main objective of this edited book is to address various challenges and issues pertinent to the intelligent control of UMVs and UAVs. (Nova)

This book offers a timely overview of nonlinear control methods applied to a set of vehicles and their applications to study vehicle dynamics. The first part on the book presents the mathematical models used for describing motion of three class of vehicles such as underwater vehicles, hovercrafts and airships. In turn, each model is expressed in terms of Inertial Quasi-Velocities. Various control strategies from the literature, including model-free ones, are then analyzed. The second part and core of the book guides readers to developing model-based control algorithms using Inertial Quasi-Velocities. Both non-adaptive and adaptive versions are covered. Each controller is validated through simulation tests, which are reported in detail. In turn, this part shows how to use the controllers to gain information about vehicle dynamics, thus describing an important relationship between the dynamics of the moving object and its motion control. The effects of mechanical couplings between variables describing vehicle motion due to inertial forces are also discussed. All in all, this book offers a timely guide and extensive information on nonlinear control schemes for unmanned marine and aerial vehicles. It covers specifically the simulation tests and is therefore meant as a starting point for engineers and researchers that would like to verify experimentally the suitability of the proposed models in real vehicles. Further, it also supports advanced-level students and educators in their courses on vehicle dynamics, control engineering and robotics.