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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.
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 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.
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.
Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties: Autonomy—Individuals that create the swarm robotic system are autonomous robots. They are independent and can interact with each other and the environment.Large number—They are in large number, enabling cooperation.Scalability and robustness—A new unit can be easily added to the system, so the system can be easily scaled. A greater number of units improves the performance of the system. The system is quite robust to the loss of some units, as some units still remain to perform, although the system will not perform to its maximum capabilities.Decentralized coordination—The robots communicate with each other and with their environment to make final decisions.Flexibility—The swarm robotic system has the ability to generate modularized solutions to different tasks.
Autonomous vehicles (AVs) have been used in military operations for more than 60 years, with torpedoes, cruise missiles, satellites, and target drones being early examples.1 They have also been widely used in the civilian sector-for example, in the disposal of explosives, for work and measurement in radioactive environments, by various offshore industries for both creating and maintaining undersea facilities, for atmospheric and undersea research, and by industry in automated and robotic manufacturing. Recent military experiences with AVs have consistently demonstrated their value in a wide range of missions, and anticipated developments of AVs hold promise for increasingly significant roles in future naval operations. Advances in AV capabilities are enabled (and limited) by progress in the technologies of computing and robotics, navigation, communications and networking, power sources and propulsion, and materials. Autonomous Vehicles in Support of Naval Operations is a forward-looking discussion of the naval operational environment and vision for the Navy and Marine Corps and of naval mission needs and potential applications and limitations of AVs. This report considers the potential of AVs for naval operations, operational needs and technology issues, and opportunities for improved operations.
This monograph presents new theories and methods for fixed-time cooperative control of multi-agent systems. Fundamental concepts of fixed-time stability and stabilization are introduced with insightful understanding. This book presents solutions for several problems of fixed-time cooperative control using systematic design methods. The book compares fixed-time cooperative control with asymptotic cooperative control, demonstrating how the former can achieve better closed-loop performance and disturbance rejection properties. It also discusses the differences from finite-time control, and shows how fixed-time cooperative control can produce the faster rate of convergence and provide an explicit estimate of the settling time independent of initial conditions. This monograph presents multiple applications of fixed-time control schemes, including to distributed optimization of multi-agent systems, making it useful to students, researchers and engineers alike.
The two volume set LNAI 10984 and LNAI 10985 constitutes the refereed proceedings of the 11th International Conference on Intelligent Robotics and Applications, ICIRA 2018, held in Newcastle, NSW, Australia, in August 2018. The 81 papers presented in the two volumes were carefully reviewed and selected from 129 submissions. The papers in the first volume of the set are organized in topical sections on multi-agent systems and distributed control; human-machine interaction; rehabilitation robotics; sensors and actuators; and industrial robot and robot manufacturing. The papers in the second volume of the set are organized in topical sections on robot grasping and control; mobile robotics and path planning; robotic vision, recognition and reconstruction; and robot intelligence and learning.