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
1. Section 1: Introduction and Preface - p. xxiv 2. Chapter One: Introduction to Complex Systems and Feedback Control - p. xxiv 3. Section 2: Adaptive Control and Neuroscience - p. 30 4. Chapter Two: Hierarchical Adaptive Control of Rapidly Time-Varying Systems Using Multiple Models - p. 30 5. Chapter Three: Adaptive Stabilization of Uncertain Systems With Model-Based Control and Event-Triggered Feedback Updates - p. 66 6. Chapter Four: A Neural Field Theory for Loss of Consciousness: Synaptic Drive Dynamics, System Stability, Attractors, Partial Synchronization, and Hopf Bifurcations Characterizing the Anesthetic Cascade - p. 92 7. Abstract - p. 93 8. 1 Introduction - p. 93 9. 2 A Model for Excitatory and Inhibitory Neural Populations - p. 96 10. 3 A Two-Class Mean Excitatory and Mean Inhibitory Synaptic Drive Model - p. 101 11. 4 Large Neural Populations, Partial Synchronization, and Suppression of Consciousness - p. 113 12. 5 Generalized Neural Population Models, Nonmonotonic Postsynaptic Potentials, and Drug Biphasic Response - p. 122 13. 6 A Second-Order Mean Excitatory and Inhibitory Synaptic Drive Model - p. 131 14. 7 Numerical Simulations of the Anesthetic Cascade Model - p. 134 15. 8 Conclusion - p. 156 16. Acknowledgments - p. 160 17. Section 3: Adaptive Learning Algorithms - p. 162 18. Chapter Five: Optimal Tracking Control of Uncertain Systems: On-Policy and Off-Policy Reinforcement Learning Approaches - p. 162 19. Chapter Six: Addressing Adaptation and Learning in the Context of Model Predictive Control With Moving-Horizon Estimation - p. 186 20. Chapter Seven: Stochastic Adaptive Dynamic Programming for Robust Optimal Control Design - p. 209 21. Abstract - p. 211 22. 1 Introduction - p. 211 23. 2 Model and Problem Formulation - p. 213 24. 3 Discount-Optimal Control - p. 216 25. 4 Bias-Optimal Control - p. 218 26. 5 Stochastic ADP Design - p. 222 27. 6 Stochastic Robust Optimal Control - p. 226 28. 7 Stochastic RADP Design - p. 231 29. 8 Illustrative Examples - p. 234 30. 9 Summary - p. 241 31. Acknowledgments - p. 242 32. Chapter Eight: Model-Based Reinforcement Learning for Approximate Optimal Regulation - p. 245 33. Chapter Nine: Continuous-Time Distributed Adaptive Dynamic Programming for Heterogeneous Multiagent Optimal Synchronization Control - p. 273 34. Abstract - p. 275 35. 1 Introduction - p. 275 36. 2 Graphs and Synchronization of Multiagent Systems - p. 278 37. 3 Optimal Distributed Synchronization Control for Heterogeneous Multiagent Differential Graphical Games - p. 281 38. 4 Heterogeneous Multiagent Differential Graphical Games by an Iterative ADP Algorithm - p. 285 39. 5 Simulation Study - p. 292 40. 6 Conclusion - p. 301 41. Chapter Ten: Model-Free Learning of Nash Games With Applications to Network Security - p. 304 42. Abstract - p. 305 43. 1 Introduction - p. 305 44. 2 Actuation Corruption Attacks - p. 308 45. 3 Communication Network Corruption (Jamming) Attacks - p. 319 46. 4 Conclusion and Future Work - p. 331 47. Section 4: Networked Systems and Cooperative Control - p. 334 48. Chapter Eleven: Adaptive Optimal Regulation of a Class of Uncertain Nonlinear Systems Using Event Sampled Neural Network Approximators - p. 334 49. Abstract - p. 337 50. 1 Introduction - p. 337 51. 2 Preliminaries - p. 340 52. 3 Adaptive Event-Triggered State Feedback Control - p. 345 53. 4 Approximate Optimal Controller Design - p. 352 54. 5 Simulations - p. 365 55. 6 Conclusions - p. 369 56. Acknowledgments - p. 370 57. Chapter Twelve: Decentralized Cooperative Control in Degraded Communication Environments - p. 372 58. Chapter Thirteen: Multiagent Layered Formation Control Based on Rigid Graph Theory - p. 395 59. Chapter Fourteen: Certainty Equivalence, Separation Principle, and Cooperative Output Regulation of Multiagent Systems by the Distributed Observer Approach - p. 419 60. Abstract - p. 421 61. 1 Introduction - p. 421 62. 2 Linear Output Regulation - p. 422 63. 3 Solvability of the Linear Output Regulation Problem - p. 427 64. 4 Linear Multiagent Systems and a Distributed Observer - p. 430 65. 5 Some Stability Results - p. 433 66. 6 Solvability of the Cooperative Linear Output Regulation Problem - p. 435 67. 7 Some Variants and Extensions - p. 441 68. 8 Concluding Remarks - p. 447 69. Acknowledgments - p. 447 70. Appendix: Graph - p. 447 71. Chapter Fifteen: Cooperative Learning for Robust Connectivity in Multirobot Heterogeneous Networks - p. 449 72. Chapter Sixteen: Discrete-Time Flocking of Wheeled Vehicles With a Large Communication Delay Through a Potential Function Approach - p. 473 73. Chapter Seventeen: Cooperative Control and Networked Operation of Passivity-Short Systems - p. 498 74. Chapter Eighteen: Synchronizing Region Approach for Identical Linear Time-Invariant Agentsa - p. 518 75. Abstract - p. 519 76. 1 Introduction - p. 519 77. 2 Graph Properties and Notation - p. 522 78. 3 System Dynamics and Control Goal - p. 522 79. 4 Sufficient Conditions for Synchronization - p. 528 80. 5 Robust Synchronizing Region for Disturbances and Uncertain Systems - p. 545 81. 6 Conclusions - p. 547 82. Section 5: Applications - p. 548 83. Chapter Nineteen: The Stereographic Product of Positive-Real Functions is Positive Real - p. 548 84. Chapter Twenty: Collective Target Tracking Mean Field Control for Markovian Jump-Driven Models of Electric Water Heating Loads - p. 558 85. Chapter Twenty-One: Trajectory Planning Based on Collocation Methods for Adaptive Motion Control of Multiple Aerial and Ground Autonomous Vehicles - p. 584 86. Chapter Twenty-Two: Intelligent Control of a Prosthetic Ankle Joint Using Gait Recognition - p. 634 87. Chapter Twenty-Three: Novel Robust Adaptive Algorithms for Estimation and Control: Theory and Practical Examples - p. 660 88. Abstract - p. 661 89. 1 Introduction - p. 661 90. 2 Review of Well-Known Adaptive Algorithms and Motivation for Increased Robustness - p. 664 91. 3 Novel Adaptive Algorithms - p. 670 92. 4 Example of Parameter Estimation in Vehicular Systems - p. 674 93. 5 Example in Control of Robotic Systems - p. 681 94. 6 Example in Adaptive Optimal Control - p. 692 95. 7 Conclusions and Further Reading - p. 704 96. Acknowledgments - p. 705 97. Author Index - p. 711 98. Subject Index - p. 725
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
1. Section 1: Introduction and Preface - p. xxiv 2. Chapter One: Introduction to Complex Systems and Feedback Control - p. xxiv 3. Section 2: Adaptive Control and Neuroscience - p. 30 4. Chapter Two: Hierarchical Adaptive Control of Rapidly Time-Varying Systems Using Multiple Models - p. 30 5. Chapter Three: Adaptive Stabilization of Uncertain Systems With Model-Based Control and Event-Triggered Feedback Updates - p. 66 6. Chapter Four: A Neural Field Theory for Loss of Consciousness: Synaptic Drive Dynamics, System Stability, Attractors, Partial Synchronization, and Hopf Bifurcations Characterizing the Anesthetic Cascade - p. 92 7. Abstract - p. 93 8. 1 Introduction - p. 93 9. 2 A Model for Excitatory and Inhibitory Neural Populations - p. 96 10. 3 A Two-Class Mean Excitatory and Mean Inhibitory Synaptic Drive Model - p. 101 11. 4 Large Neural Populations, Partial Synchronization, and Suppression of Consciousness - p. 113 12. 5 Generalized Neural Population Models, Nonmonotonic Postsynaptic Potentials, and Drug Biphasic Response - p. 122 13. 6 A Second-Order Mean Excitatory and Inhibitory Synaptic Drive Model - p. 131 14. 7 Numerical Simulations of the Anesthetic Cascade Model - p. 134 15. 8 Conclusion - p. 156 16. Acknowledgments - p. 160 17. Section 3: Adaptive Learning Algorithms - p. 162 18. Chapter Five: Optimal Tracking Control of Uncertain Systems: On-Policy and Off-Policy Reinforcement Learning Approaches - p. 162 19. Chapter Six: Addressing Adaptation and Learning in the Context of Model Predictive Control With Moving-Horizon Estimation - p. 186 20. Chapter Seven: Stochastic Adaptive Dynamic Programming for Robust Optimal Control Design - p. 209 21. Abstract - p. 211 22. 1 Introduction - p. 211 23. 2 Model and Problem Formulation - p. 213 24. 3 Discount-Optimal Control - p. 216 25. 4 Bias-Optimal Control - p. 218 26. 5 Stochastic ADP Design - p. 222 27. 6 Stochastic Robust Optimal Control - p. 226 28. 7 Stochastic RADP Design - p. 231 29. 8 Illustrative Examples - p. 234 30. 9 Summary - p. 241 31. Acknowledgments - p. 242 32. Chapter Eight: Model-Based Reinforcement Learning for Approximate Optimal Regulation - p. 245 33. Chapter Nine: Continuous-Time Distributed Adaptive Dynamic Programming for Heterogeneous Multiagent Optimal Synchronization Control - p. 273 34. Abstract - p. 275 35. 1 Introduction - p. 275 36. 2 Graphs and Synchronization of Multiagent Systems - p. 278 37. 3 Optimal Distributed Synchronization Control for Heterogeneous Multiagent Differential Graphical Games - p. 281 38. 4 Heterogeneous Multiagent Differential Graphical Games by an Iterative ADP Algorithm - p. 285 39. 5 Simulation Study - p. 292 40. 6 Conclusion - p. 301 41. Chapter Ten: Model-Free Learning of Nash Games With Applications to Network Security - p. 304 42. Abstract - p. 305 43. 1 Introduction - p. 305 44. 2 Actuation Corruption Attacks - p. 308 45. 3 Communication Network Corruption (Jamming) Attacks - p. 319 46. 4 Conclusion and Future Work - p. 331 47. Section 4: Networked Systems and Cooperative Control - p. 334 48. Chapter Eleven: Adaptive Optimal Regulation of a Class of Uncertain Nonlinear Systems Using Event Sampled Neural Network Approximators - p. 334 49. Abstract - p. 337 50. 1 Introduction - p. 337 51. 2 Preliminaries - p. 340 52. 3 Adaptive Event-Triggered State Feedback Control - p. 345 53. 4 Approximate Optimal Controller Design - p. 352 54. 5 Simulations - p. 365 55. 6 Conclusions - p. 369 56. Acknowledgments - p. 370 57. Chapter Twelve: Decentralized Cooperative Control in Degraded Communication Environments - p. 372 58. Chapter Thirteen: Multiagent Layered Formation Control Based on Rigid Graph Theory - p. 395 59. Chapter Fourteen: Certainty Equivalence, Separation Principle, and Cooperative Output Regulation of Multiagent Systems by the Distributed Observer Approach - p. 419 60. Abstract - p. 421 61. 1 Introduction - p. 421 62. 2 Linear Output Regulation - p. 422 63. 3 Solvability of the Linear Output Regulation Problem - p. 427 64. 4 Linear Multiagent Systems and a Distributed Observer - p. 430 65. 5 Some Stability Results - p. 433 66. 6 Solvability of the Cooperative Linear Output Regulation Problem - p. 435 67. 7 Some Variants and Extensions - p. 441 68. 8 Concluding Remarks - p. 447 69. Acknowledgments - p. 447 70. Appendix: Graph - p. 447 71. Chapter Fifteen: Cooperative Learning for Robust Connectivity in Multirobot Heterogeneous Networks - p. 449 72. Chapter Sixteen: Discrete-Time Flocking of Wheeled Vehicles With a Large Communication Delay Through a Potential Function Approach - p. 473 73. Chapter Seventeen: Cooperative Control and Networked Operation of Passivity-Short Systems - p. 498 74. Chapter Eighteen: Synchronizing Region Approach for Identical Linear Time-Invariant Agentsa - p. 518 75. Abstract - p. 519 76. 1 Introduction - p. 519 77. 2 Graph Properties and Notation - p. 522 78. 3 System Dynamics and Control Goal - p. 522 79. 4 Sufficient Conditions for Synchronization - p. 528 80. 5 Robust Synchronizing Region for Disturbances and Uncertain Systems - p. 545 81. 6 Conclusions - p. 547 82. Section 5: Applications - p. 548 83. Chapter Nineteen: The Stereographic Product of Positive-Real Functions is Positive Real - p. 548 84. Chapter Twenty: Collective Target Tracking Mean Field Control for Markovian Jump-Driven Models of Electric Water Heating Loads - p. 558 85. Chapter Twenty-One: Trajectory Planning Based on Collocation Methods for Adaptive Motion Control of Multiple Aerial and Ground Autonomous Vehicles - p. 584 86. Chapter Twenty-Two: Intelligent Control of a Prosthetic Ankle Joint Using Gait Recognition - p. 634 87. Chapter Twenty-Three: Novel Robust Adaptive Algorithms for Estimation and Control: Theory and Practical Examples - p. 660 88. Abstract - p. 661 89. 1 Introduction - p. 661 90. 2 Review of Well-Known Adaptive Algorithms and Motivation for Increased Robustness - p. 664 91. 3 Novel Adaptive Algorithms - p. 670 92. 4 Example of Parameter Estimation in Vehicular Systems - p. 674 93. 5 Example in Control of Robotic Systems - p. 681 94. 6 Example in Adaptive Optimal Control - p. 692 95. 7 Conclusions and Further Reading - p. 704 96. Acknowledgments - p. 705 97. Author Index - p. 711 98. Subject Index - p. 725
