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Full Description
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
Contents
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
