Control of Complex Systems: Theory and Applications

In the era of cyber-physical systems, the area of control of complex systems has grown to be one of the hardest in terms of algorithmic design techniques and analytical tools. The 23 chapters, written by international specialists in the field, cover a variety of interests within the broader field of learning, adaptation, optimization and networked control. The editors have grouped these into the following 5 sections: “Introduction and Background on Control Theory”, “Adaptive Control and Neuroscience”, “Adaptive Learning Algorithms”, “Cyber-Physical Systems and Cooperative Control”, “Applications”.The diversity of the research presented gives the reader a unique opportunity to explore a comprehensive overview of a field of great interest to control and system theorists.

This book is intended for researchers and control engineers in machine learning, adaptive control, optimization and automatic control systems, including Electrical Engineers, Computer Science Engineers, Mechanical Engineers, Aerospace/Automotive Engineers, and Industrial Engineers. It could be used as a text or reference for advanced courses in complex control systems.

• Collection of chapters from several well-known professors and researchers that will showcase their recent work

• Presents different state-of-the-art control approaches and theory for complex systems

• Gives algorithms that take into consideration the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals and malicious attacks compromising the security of networked teams

• Real system examples and figures throughout, make ideas concrete

Kyriakos G. Vamvoudakis is the Dutton-Ducoffe Endowed Professor at The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. He holds a secondary appointment in the School of Electrical and Computer Engineering. His expertise is in reinforcement learning, control theory, game theory, cyber-physical security, bounded rationality, and safe/assured autonomy.He has served on various international program committees and has organized several international conferences. He currently is a member of the Technical Committee on Intelligent Control of the IEEE Control Systems Society, a member of the Technical Committee on Adaptive Dynamic Programming and Reinforcement Learning of the IEEE Computational Intelligence Society, a member of the IEEE Control Systems Society Conference Editorial Board, an Associate Editor of: Automatica; IEEE Transactions on Automatic Control; IEEE Transactions on Neural Networks and Learning Systems; IEEE Computational Intelligence Magazine; IEEE Transactions on Systems, Man, and Cybernetics: Systems; IEEE Transactions on Artificial Intelligence; Neurocomputing; Journal of Optimization Theory and Applications; and of Frontiers in Control Engineering-Adaptive, Robust and Fault Tolerant Control. He had also served as a Guest Editor for, IEEE Transactions on Automation Science and Engineering (Special issue on Learning from Imperfect Data for Industrial Automation); IEEE Transactions on Neural Networks and Learning Systems (Special issue on Reinforcement Learning Based Control: Data-Efficient and Resilient Methods); IEEE Transactions on Industrial Informatics (Special issue on Industrial Artificial Intelligence for Smart Manufacturing); and IEEE Transactions on Intelligent Transportation Systems (Special issue on Unmanned Aircraft System Traffic Management). He is also a registered Electrical/Computer engineer (PE), a member of the Technical Chamber of Greece, an Associate Fellow of AIAA, and a Senior Member of IEEE.

Dr. Jagannathan Sarangapani (referred here as S. Jagannathan) is at the Missouri University of Science and Technology (former University of Missouri-Rolla) where he is a Rutledge-Emerson Endowed Chair Professor of Electrical and Computer Engineering and Site Director for the NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems. His research interests include neural network control, adaptive event-triggered control, secure networked control systems, prognostics, and autonomous systems/robotics.