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Distributed Cooperative Control

Emerging Applications


Yi Guo









To My Parents

Kuisheng Guo and Zhenfu Liu


Networked systems are most familiar in the form of data networks connecting computers (e.g., Internet) or networks for voice communications. However, in the broader sense, networks provide the infrastructure for spatially distributed components of a system, or distributed systems, to intercommunicate, enabling powerful capabilities for cooperative and collective behaviors for the overall system. Such collective behaviors are seen in man‐made systems (e.g., large power systems and transportation networks) as well as in naturally occurring systems (e.g., flocks of birds, clusters of biological organisms, and gene networks). The remarkable advances in networking technologies (both wired and wireless networks) are enabling a wide range of new, man‐made systems based on sophisticated and low cost distributed components cooperating across low cost and high‐speed data networks. Also, the availability of new tools and the development of new techniques in handling multi‐scale systems (from micro aerial vehicles to nanoparticles) give unprecedented opportunity for breakthroughs in controlling realworld networked systems.

Cooperative control of multi‐agent systems has experienced rapid development during the past few decades. Distributed consensus algorithms received much research attention, and cooperative control of multi‐vehicle systems have been extensively studied extending information consensus to coordinated motions of autonomous vehicles. Due to domain specifications and system constraints, applications found in realworld systems pose new challenges to cooperative control. The richness of realworld problems and diverse application domains revitalize the field and stimulate new research directions.

This book presents applications of distributed cooperative control to realworld engineering and physics systems addressing emerging needs for high efficiency distributed control systems. After introducing backgrounds and reviewing fundamental distributed consensus algorithms, technical contents of the book are divided into three parts. The first part deals with networked communication systems, and consists of three chapters, which present distributed consensus for quantized communication, cooperative spectrum sensing and distributed radio environment mapping for cognitive radio networks. The second part of the book presents cooperative control of multi‐robotic systems, and includes two chapters to discuss the source seeking and plume tracking problems by distributed cooperating robots. In the third part of the book, cooperative control of multi‐agent physics systems are addressed, which includes two chapters on friction control of coupled nanoparticles and synchronization of coupled laser arrays. Despite variations in system scales (from macroscale robots to nanoscale particles) and differences of application domains (from electrical to physics systems), the fundamental control issue to maintain coordination or synchronization of system components is universal, which is captured either by the fundamental concept of consensus/agreement, or through information diffusion over a network to obtain distributed estimation of global information. The graph matrix tools are also instrumental to stability and convergence analysis throughout the book.

The results reported in the book summarize the research effort on cooperative control applications of my research group at Stevens Institute of Technology during the past few years. The graduate students, Shuai Li, Wenlin Zhang, Ruofan Kong, and the postdoctoral researcher, Zheng Wang, directly contributed to the research reported in the book. The book uses materials from the Ph.D. dissertations of Shuai Li and Wenlin Zhang. The reported work was generated through research collaborations with my colleagues at Stevens and other institutions including Hongbin Li (Stevens), Yingying Chen (Stevens), Zhihua Qu (University of Central Florida), Yehuda Braiman (Oak Ridge National Laboratory), Brian Bingham (University of Hawaii and Naval Postgraduate School), Joseph Mitola III (Federated Wireless), and Zhenyu Zhang (University of Science and Technology of China). I would like to thank my students for their hard work, and thank my collaborators for introducing me to control problems originated from other fields.

Acknowledgment is given to National Science Foundation for providing funding to support this work during the past few years under Grants CMMI‐0825613, EFRI‐1024660, IIS‐1218155, CNS‐1318748, and IIS‐1527016. Finally, I want to thank my parents (Kuisheng Guo and Zhenfu Liu), my husband (Weimin), and my kids (Raymond and Victoria) for their love and support.

April 2016Yi Guo
Hoboken, New Jersey

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