This volume contains the contributions to a Workshop on Group Coordination and Cooperative Control held in Tromsø, Norway, 2006, to focus on control theoretic challenges raised by group coordination and cooperation, and lay a foundation for future research. The book covers a wide range of subjects within the area of group coordination and cooperative control, and forms a valuable and up-to-date text on the newer trends in group coordination and cooperative control.

There are two ways people coordinate their actions: through cooperation, exercised by economic power, and through control, exercised by political power. When economic and political power are held by the same people, the result is stagnation; when those who hold economic power are not the same people who hold political power, the result is progress. This book presents the ways in which economic power and political power can be separated, and how they can remain so, by analyzing the nature of power and the differences between economic and political power. The book then discusses the history of economic and political power, including hunter-gatherer societies, agrarian societies, and modern commercial and industrial societies. This background lends insight into why political and economic power were typically held by the same people, and why recently those without political power have been able to acquire economic power. Incentives play a key role in understanding how those two types of power can become separated, and why there is always a tendency for them to recombine. But ideas also play a crucial role, including the influence of the Enlightenment, on the progress that has occurred in the last several hundred years.

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.

"The purpose of this book is to present the theory of control and coordination in hierarchical systems - that is, in systems where the decision-making responsibility has been divided. Since it aims to present theory that will be useful for applications, it not only encompasses the basic, general, and consequently somewhat abstract principles of coordination, but also considers such practical features as differences between models and the reality they describe, constraints, possible use of feedback information, and time horizons." --Preface.

This comprehensive edited treatise discusses the neurological, physiological, and cognitive aspects of interlimb coordination. It is unique in promoting a multidisciplinary perspective through introductory chapter contributions from experts in the neurosciences, experimental and developmental psychology, and kinesiology. Beginning with chapters defining the neural basis of interlimb coordination in animals, the book progresses toward an understanding of human locomotor control and coordination and the underlying brain structures and nerves that make such control possible. Section two focuses on the dynamics of interlimb coordination and the physics of movement. The final section presents information on how practice and experience affect coordination, including general skill acquisition, learning to walk, and the process involved in rhythmic tapping.

Overview Recent years have seen an increasing interest in the development of multi-sensory robot systems. The reason for this interest stems from a realization that there are fundamental limitations on the reconstruction of environment descriptions using only a single source of sensor information. If robot systems are ever to achieve a degree of intelligence and autonomy, they must be capable of using many different sources of sensory information in an active and dynamic manner. The observations made by the different sensors of a multi-sensor system are always uncertain, usually partial, occasionally spuri9us or incorrect and often geographically or geometrically imcomparable with other sensor views. The sensors of these systems are characterized by the diversity of information that they can provide and by the complexity of their operation. It is the goal of a multi sensor system to combine information from all these different sources into a robust and consistent description of the environment.

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.