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This book describes how control of distributed systems can be advanced by an integration of control, communication, and computation. The global control objectives are met by judicious combinations of local and nonlocal observations taking advantage of various forms of communication exchanges between distributed controllers. Control architectures are considered according to increasing degrees of cooperation of local controllers: fully distributed or decentralized control, control with communication between controllers, coordination control, and multilevel control. The book covers also topics bridging computer science, communication, and control, like communication for control of networks, average consensus for distributed systems, and modeling and verification of discrete and of hybrid systems. Examples and case studies are introduced in the first part of the text and developed throughout the book. They include: control of underwater vehicles, automated-guided vehicles on a container terminal, control of a printer as a complex machine, and control of an electric power system. The book is composed of short essays each within eight pages, including suggestions and references for further research and reading. By reading the essays collected in the book Coordination Control of Distributed Systems, graduate students and post-docs will be introduced to the research frontiers in control of decentralized and of distributed systems. Control theorists and practitioners with backgrounds in electrical, mechanical, civil and aerospace engineering will find in the book information and inspiration to transfer to their fields of interest the state-of-art in coordination control.
Distributed Coordination of Multi-agent Networks introduces problems, models, and issues such as collective periodic motion coordination, collective tracking with a dynamic leader, and containment control with multiple leaders, and explores ideas for their solution. Solving these problems extends the existing application domains of multi-agent networks; for example, collective periodic motion coordination is appropriate for applications involving repetitive movements, collective tracking guarantees tracking of a dynamic leader by multiple followers in the presence of reduced interaction and partial measurements, and containment control enables maneuvering of multiple followers by multiple leaders.
Multiagent systems (MAS) are one of the most exciting and the fastest growing domains in the intelligent resource management and agent-oriented technology, which deals with modeling of autonomous decisions making entities. Recent developments have produced very encouraging results in the novel approach of handling multiplayer interactive systems. In particular, the multiagent system approach is adapted to model, control, manage or test the operations and management of several system applications including multi-vehicles, microgrids, multi-robots, where agents represent individual entities in the network. Each participant is modeled as an autonomous participant with independent strategies and responses to outcomes. They are able to operate autonomously and interact pro-actively with their environment. In recent works, the problem of information consensus is addressed, where a team of vehicles communicate with each other to agree on key pieces of information that enable them to work together in a coordinated fashion. The problem is challenging because communication channels have limited range and there are possibilities of fading and dropout. The book comprises chapters on synchronization and consensus in multiagent systems. It shows that the joint presentation of synchronization and consensus enables readers to learn about similarities and differences of both concepts. It reviews the cooperative control of multi-agent dynamical systems interconnected by a communication network topology. Using the terminology of cooperative control, each system is endowed with its own state variable and dynamics. A fundamental problem in multi-agent dynamical systems on networks is the design of distributed protocols that guarantee consensus or synchronization in the sense that the states of all the systems reach the same value. It is evident from the results that research in multiagent systems offer opportunities for further developments in theoretical, simulation and implementations. This book attempts to fill this gap and aims at presenting a comprehensive volume that documents theoretical aspects and practical applications.
As artificial intelligence (AI) is applied to more complex problems and a wider set of applications, the ability to take advantage of the computational power of distributed and parallel hardware architectures and to match these architec tures with the inherent distributed aspects of applications (spatial, functional, or temporal) has become an important research issue. Out of these research concerns, an AI subdiscipline called distributed problem solving has emerged. Distributed problem-solving systems are broadly defined as loosely-coupled, distributed networks of semi-autonomous problem-solving agents that perform sophisticated problem solving and cooperatively interact to solve problems. N odes operate asynchronously and in parallel with limited internode commu nication. Limited internode communication stems from either inherent band width limitations of the communication medium or from the high computa tional cost of packaging and assimilating information to be sent and received among agents. Structuring network problem solving to deal with consequences oflimited communication-the lack of a global view and the possibility that the individual agents may not have all the information necessary to accurately and completely solve their subproblems-is one of the major focuses of distributed problem-solving research. It is this focus that also is one of the important dis tinguishing characteristics of distributed problem-solving research that sets it apart from previous research in AI.
Papers collected here, from a December 2001 workshop held at the University of Central Florida, examine topics related to process coordination and ubiquitous computing. Papers on coordination models discuss areas such as space-based coordination and open distributed systems, global virtual data stru
Papers collected here, from a December 2001 workshop held at the University of Central Florida, examine topics related to process coordination and ubiquitous computing. Papers on coordination models discuss areas such as space-based coordination and open distributed systems, global virtual data structures, models of coordination and Web-based systems, and a coordination model for secure collaboration. Material on grid coordination examines the complexity of scheduling and coordination on computational grids, programming the grid with distributed objects, data sharing through virtual file systems, and dynamic data-driven application systems. Papers on intelligent coordination and ubiquitous computing look at terraforming cyberspace, thin middleware, and Internet security. There is no subject index. Annotation copyrighted by Book News, Inc., Portland, OR
This book investigates the distributed impulsive coordination control of multi-agent systems (MASs), including consensus problem and formation problem. Compared with continuous control, impulsive control as a discontinuous control method only needs information interaction at impulsive instants, which can greatly reduce the transmission cost in the communication process. In practice, MASs are often affected by environmental and hardware conditions, resulting in the application of impulsive control is limited. For example, it is difficult to exert the impulsive control signal accurately at a fixed instant, and it is difficult to control all agents at the same impulsive instant. Moreover, the actual models of MASs often contain some constraints such as unknown parameters, stochastic perturbation, and time delays. In addition, stochastic switching topologies and cyberattacks will limit the communication between agents in MASs, which will greatly affect the coordination control performance. Based on the existing research results, this book considers the above constraints of MASs in practice and studies the key problems of distributed impulsive coordination control for MASs. The book is intended for undergraduate and graduate students who are interested in control system theory, as well as engineers studying MASs.
Distributed Intelligent Systems: A Coordination Perspective comprehensively answers commonly asked questions about coordination in agent-oriented distributed systems. Characterizing the state-of-the-art research in the field of coordination with regard to the development of distributed agent-oriented systems is a particularly complex endeavour; while existing books deal with specific aspects of coordination, the major contribution of this book lies in the attempt to provide an in-depth review covering a wide range of issues regarding multi-agent coordination in Distributed Artificial Intelligence. Key features: Unveils the lack of coherence and order that characterizes the area of research pertaining to coordination of distributed intelligent systems Examines coordination models, frameworks, strategies and techniques to enable the development of distributed intelligent agent-oriented systems Provides specific recommendations to realize more widespread deployment of agent-based systems