Control of Complex Systems: Theory and Applications

Control of Complex Systems: Theory and Applications contains a collection of articles by friends, co-authors, colleagues, and former PhD students of Frank L. Lewis. The 25 chapters, written by international specialists in the field, cover a variety of interests within the broader field of learning, adaptation, and control. The editors have grouped these into the following five sections: Background and Open Topics, Adaptive Control and Neuroscience, Adaptive Learning Algorithms for Control and Diagnosis, Cooperative Control, Applications and Other Related Topics. 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 for researchers and control engineers working with machine learning, adaptive control, 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 learning and adaptive control systems, or learning feedback control. Includes chapters from several well-known professors and researchers that showcases their recent workPresents different state-of-the-art control approaches and theory for complex systemsExplores 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 teamsServes as a helpful reference for researchers and control engineers working with machine learning, adaptive control, and automatic control systems INDICE: Chapter 1: Introduction and Background on Control Theory Section 1 (New Directions and Open Topics) Chapter 2: Recent Advances and New Directions in Integral Reinforcement Learning for Feedback Control Chapter 3: Hierarchical Adaptive Control of Rapidly Time-Varying Systems Chapter 4:  The Stereographic Product of Positive-Real Functions is Positive-Real Section 2 (Neuroscience, Learning and Discrete-Event Systems) Chapter 5: Addressing adaptation and learning in the context of MPC and MHE Chapter 6:Performance Regulation in Discrete Event Dynamic Systems Chapter 7: Adaptive stabilization of uncertain systems with model-based control and event-triggered feedback updates Chapter 8:  Control Architectures for Adaptive Stabilization of Dynamical Systems with Sensor Uncertainties Section 3 Reinforcement Learning Algorithms Chapter 9: Stochastic Adaptive Dynamic Programming for Robust Optimal Control Design Chapter 10: Model-based reinforcement learning for real-time approximate optimal control Chapter 11: Unsupervised Feature Learning for Policy Iteration based on Manifold Regularization Chapter 12: Model-Free Online Optimal Adaptive Control and Non-Zero Sum Games with Applications to Network Security Section 4 Cyber-Physical Systems Chapter 13:  Distributed fault diagnosis of large-scale interconnected systems Chapter 14: Adaptive optimal regulation of a class of uncertain nonlinear systems using event sampled neural network approximators Chapter 15: Multi-Agent Control in Degraded Communication Environments Chapter 16: , Multi-Agent Layered Formation Control Based on Rigid Graph Theory Section 5 Cooperative Control Chapter 17: Cooperative Control and Networked Operation of Passivity-Short Systems Chapter 18: Cooperative Output Regulation Problem of Linear Multi-Agent Systems by the Distributed Observer Approach Chapter 19: Cooperative Learning for Robust Connectivity in Multi-robot Heterogeneous Networks Chapter 20: Discrete-time Coordinated Control of Wheeled Vehicles in the Presence of a Large Communication Delay Section 6 Applications of Adaptive Control and Game Theory Chapter 21: Control of Aggregate Electric Water Heating Loads via Mean Field Games Based Methods Chapter 22: Estimation and Control for Safe Autonomous Vehicle Navigation in Industrial Warehousing Environments Chapter 23: Intelligent control of a prosthetic ankle using gait recognition Chapter 24: An adaptive algorithm for robust, finite time control and estimation -Examples in theory and practice Chapter 24: Conclusions

• ISBN: 978-0-12-805246-4
• Editorial: Butterworth-Heinemann
• Encuadernacion: Cartoné
• Páginas: 752
• Fecha Publicación: 01/08/2016
• Nº Volúmenes: 1
• Idioma: Inglés