Control of Complex Systems: Theory and Applications

ISBN-13: 9780128052464 / Angielski / Twarda / 2016 / 762 str.

Sarangapani Jagannathan

Opis

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

• Includes chapters from several well-known professors and researchers that showcases their recent work
• Presents different state-of-the-art control approaches and theory for complex systems
• Explores 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
• Serves as a helpful reference for researchers and control engineers working with machine learning, adaptive control, and automatic control systems

Spis treści

1. Introduction and Background on Control Theory    2. Hierarchical Adaptive Control of Rapidly Time-Varying Systems    3. Adaptive stabilization of uncertain systems with model-based control and event-triggered feedback updates    4. A Neural Field Theory for Loss of Consciousness: Synaptic Drive Dynamics, System Stability, Attractors, Partial Synchronization, and Hopf Bifurcations Characterizing the Anesthetic Cascade

5. Optimal Tracking Control of Uncertain Systems: On-policy and Off-policy Reinforcement Learning Approaches

6. Addressing adaptation and learning in the context of MPC and MHE

7. Stochastic Adaptive Dynamic Programming for Robust Optimal Control Design    8. Model-based reinforcement learning for approximate optimal regulation

9. Continuous-Time Distributed Adaptive Dynamic Programming for Heterogeneous Multi-Agent Optimal Synchronization Control   10. Model-Free Learning of Games with Applications to Network Security

11. Adaptive Optimal Regulation of a Class of Uncertain Nonlinear Systems using Event Sampled Neural Network Approximators    12. Decentralized Cooperative Control in Degraded Communication Environments   13. Multi-Agent Layered Formation Control Based on Rigid Graph Theory    14. Certainty Equivalence, Separation Principle, and Cooperative Output Regulation of Multi-Agent Systems by Distributed Observer Approach

  15. Cooperative Learning for Robust Connectivity in Multi-robot Heterogeneous Networks    16. Flocking of Discrete-time Wheeled Vehicles with a Large Communication Delay Through a Potential Functional Approach

17. Cooperative Control and Networked Operation of Passivity-Short Systems    18. Synchronizing Region Approach for Identical Linear Time-invariant Agents Applications

19. The Stereographic Product of Positive-Real Functions is Positive-Real   20. Control of Aggregate Electric Water Heating Loads via Mean Field Games Based Methods    21. Trajectory Planning Based on Collocation Methods for Adaptive Motion Control of Multiple Aerial and Ground Autonomous Vehicles    22. Intelligent control of a prosthetic ankle using gait recognition    23. Novel robust adaptive algorithms for estimation and control - Theory and Practical Examples    24. Conclusions

O autorze

Kyriakos G. Vamvoudakis was born in Athens, Greece. He received the Diploma (a 5-year degree, equivalent to a Master of Science) in Electronic and Computer Engineering from the Technical University of Crete, Greece in 2006 with highest honors. After moving to the United States of America, he studied at The University of Texas at Arlington with Frank L. Lewis as his advisor, and he received his M.S. and Ph.D. in Electrical Engineering in 2008 and 2011 respectively. From May 2011 to January 2012, he was working as an Adjunct Professor and Faculty Research Associate at the University of Texas at Arlington and at the Automation and Robotics Research Institute. During the period from 2012 to 2016 he was project research scientist at the Center for Control, Dynamical Systems and Computation at the University of California, Santa Barbara. He was an assistant professor at the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech until 2018. He currently serves as 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. Dr. Vamvoudakis is the recipient of a 2019 ARO YIP award, a 2018 NSF CAREER award, a 2018 DoD Minerva Research Initiative Award, a 2021 GT Chapter Sigma Xi Young Faculty Award and his work has been recognized with best paper nominations and several international awards including the 2016 International Neural Network Society Young Investigator (INNS) Award, the Best Paper Award for Autonomous/Unmanned Vehicles at the 27th Army Science Conference in 2010, the Best Presentation Award at the World Congress of Computational Intelligence in 2010, and the Best Researcher Award from the Automation and Robotics Research Institute in 2011.

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.