Rapid advances in information processing, communication and sensing technologies have enabled more and more devices to be provided with embedded processors, networking capabilities and sensors. For the field of estimation and control, it is now possible to consider an architecture in which many simple components communicate and cooperate to achieve a joint team goal. This distributed (or networked) architecture promises much in terms of performance, reliability and simplicity of design. However, at the same time, it requires extending the traditional theories of control, communication and computation and, in fact, looking at a unified picture of the three fields.
From an estimation and control perspective, the presence of real communication channels can lead to a significant performance loss due to the introduction of non-classical information patterns into the problem. This book deals with new design principles to counter such performance degradation. The chief idea explored in this book is the joint design of information flow and the control law. While traditional control design has concentrated on calculating the optimal control input by assuming a particular information flow between the components, our approach seeks to synthesize the optimal information flow along with the optimal control law that satisfies the constraints of the information flow. Thus besides the question of What should an agent do?, the questions of Whom should an agent talk to?, What should an agent communicate?, When should an agent communicate? and so on also have to be answered. The design of the information flow represents an important degree of freedom available to the system designer that has hitherto largely been ignored. As we demonstrate in the book, the joint design of information flow and the optimal control input satisfying the constraints of that information flow yields large improvements in performance over simply trying to fit traditional design theories on distributed systems.