ANURADHA M. ANNASWAMY, PhD, is a Senior Research Scientist at the Massachusetts Institute of Technology, USA.

PRAMOD P. KHARGONEKAR, PhD, is Vice Chancellor for Research and a Distinguished Professor of Electrical Engineering and Computer Science at the University of California, Irvine, USA.

FRANÇOISE LAMNABHI-LAGARRIGUE, PhD, is a Distinguished Research Fellow at Laboratoire des Signaux et Systèmes CNRS, CentraleSupelec, Paris-Saclay University, France.

SARAH K. SPURGEON, PhD, is the Head of the Department of Electronic and Electrical Engineering and Professor of Control Engineering at University College London, UK.

A Note from the Series Editor xvii About the Editors xviii List of Contributors xix Introduction xxvii Part I Fundamental Concepts and Methods 1 1 Human-in-the-Loop Control and Cyber-Physical-Human Systems: Applications and Categorization 3Tariq Samad 1.1 Introduction 3 1.2 Cyber + Physical + Human 4 1.2.1 Cyberphysical Systems 5 1.2.2 Physical-Human Systems 6 1.2.3 Cyber-Human Systems 6 1.3 Categorizing Human-in-the-Loop Control Systems 6 1.3.1 Human-in-the-Plant 8 1.3.2 Human-in-the-Controller 8 1.3.3 Human-Machine Control Symbiosis 10 1.3.4 Humans-in-Multiagent-Loops 11 1.4 A Roadmap for Human-in-the-Loop Control 13 1.4.1 Self- and Human-Driven Cars on Urban Roads 13 1.4.2 Climate Change Mitigation and Smart Grids 14 1.5 Discussion 15 1.5.1 Other Ways of Classifying Human-in-the-Loop Control 15 1.5.2 Modeling Human Understanding and Decision-Making 16 1.5.3 Ethics and CPHS 18 1.6 Conclusions 19 Acknowledgments 19 References 20 2 Human Behavioral Models Using Utility Theory and Prospect Theory 25Anuradha M. Annaswamy and Vineet Jagadeesan Nair 2.1 Introduction 25 2.2 Utility Theory 26 2.2.1 An Example 27 2.3 Prospect Theory 27 2.3.1 An Example: CPT Modeling for SRS 30 2.3.1.1 Detection of CPT Effects via Lotteries 32 2.3.2 Theoretical Implications of CPT 33 2.3.2.1 Implication I: Fourfold Pattern of Risk Attitudes 34 2.3.2.2 Implication II: Strong Risk Aversion Over Mixed Prospects 36 2.3.2.3 Implication III: Effects of Self-Reference 37 2.4 Summary and Conclusions 38 Acknowledgments 39 References 39 3 Social Diffusion Dynamics in Cyber-Physical-Human Systems 43Lorenzo Zino and Ming Cao 3.1 Introduction 43 3.2 General Formalism for Social Diffusion in CPHS 45 3.2.1 Complex and Multiplex Networks 45 3.2.2 General Framework for Social Diffusion 46 3.2.3 Main Theoretical Approaches 48 3.3 Modeling Decision-Making 49 3.3.1 Pairwise Interaction Models 49 3.3.2 Linear Threshold Models 52 3.3.3 Game-Theoretic Models 53 3.4 Dynamics in CPHS 55 3.4.1 Social Diffusion in Multiplex Networks 56 3.4.2 Co-Evolutionary Social Dynamics 58 3.5 Ongoing Efforts Toward Controlling Social Diffusion and Future Challenges 62 Acknowledgments 63 References 63 4 Opportunities and Threats of Interactions Between Humans and Cyber-Physical Systems - Integration and Inclusion Approaches for Cphs 71Frédéric Vanderhaegen and Victor Díaz Benito Jiménez 4.1 CPHS and Shared Control 72 4.2 "Tailor-made" Principles for Human-CPS Integration 73 4.3 "All-in-one" based Principles for Human-CPS Inclusion 74 4.4 Dissonances, Opportunities, and Threats in a CPHS 76 4.5 Examples of Opportunities and Threats 79 4.6 Conclusions 85 References 86 5 Enabling Human-Aware Autonomy Through Cognitive Modeling and Feedback Control 91Neera Jain, Tahira Reid, Kumar Akash, Madeleine Yuh, and Jacob Hunter 5.1 Introduction 91 5.1.1 Important Cognitive Factors in HAI 92 5.1.2 Challenges with Existing CPHS Methods 93 5.1.3 How to Read This Chapter 95 5.2 Cognitive Modeling 95 5.2.1 Modeling Considerations 95 5.2.2 Cognitive Architectures 97 5.2.3 Computational Cognitive Models 98 5.2.3.1 ARMAV and Deterministic Linear Models 99 5.2.3.2 Dynamic Bayesian Models 99 5.2.3.3 Decision Analytical Models 100 5.2.3.4 POMDP Models 102 5.3 Study Design and Data Collection 103 5.3.1 Frame Research Questions and Identify Variables 104 5.3.2 Formulate Hypotheses or Determine the Data Needed 105 5.3.2.1 Hypothesis Testing Approach 105 5.3.2.2 Model Training Approach 105 5.3.3 Design Experiment and/or Study Scenario 107 5.3.3.1 Hypothesis Testing Approach 107 5.3.3.2 Model Training Approach 107 5.3.4 Conduct Pilot Studies and Get Initial Feedback; Do Preliminary Analysis 108 5.3.5 A Note about Institutional Review Boards and Recruiting Participants 109 5.4 Cognitive Feedback Control 109 5.4.1 Considerations for Feedback Control 110 5.4.2 Approaches 111 5.4.2.1 Heuristics-Based Planning 111 5.4.2.2 Measurement-Based Feedback 112 5.4.2.3 Goal-Oriented Feedback 112 5.4.2.4 Case Study 112 5.4.3 Evaluation Methods 113 5.5 Summary and Opportunities for Further Investigation 113 5.5.1 Model Generalizability and Adaptability 114 5.5.2 Measurement of Cognitive States 114 5.5.3 Human Subject Study Design 114 References 115 6 Shared Control with Human Trust and Workload Models 125Murat Cubuktepe, Nils Jansen, and Ufuk Topcu 6.1 Introduction 125 6.1.1 Review of Shared Control Methods 126 6.1.2 Contribution and Approach 127 6.1.3 Review of IRL Methods Under Partial Information 128 6.1.3.1 Organization 129 6.2 Preliminaries 129 6.2.1 Markov Decision Processes 129 6.2.2 Partially Observable Markov Decision Processes 130 6.2.3 Specifications 130 6.3 Conceptual Description of Shared Control 131 6.4 Synthesis of the Autonomy Protocol 132 6.4.1 Strategy Blending 132 6.4.2 Solution to the Shared Control Synthesis Problem 133 6.4.2.1 Nonlinear Programming Formulation for POMDPs 133 6.4.2.2 Strategy Repair Using Sequential Convex Programming 134 6.4.3 Sequential Convex Programming Formulation 135 6.4.4 Linearizing Nonconvex Problem 135 6.4.4.1 Linearizing Nonconvex Constraints and Adding Slack Variables 135 6.4.4.2 Trust Region Constraints 136 6.4.4.3 Complete Algorithm 136 6.4.4.4 Additional Specifications 136 6.4.4.5 Additional Measures 137 6.5 Numerical Examples 137 6.5.1 Modeling Robot Dynamics as POMDPs 138 6.5.2 Generating Human Demonstrations 138 6.5.3 Learning a Human Strategy 139 6.5.4 Task Specification 139 6.5.5 Results 140 6.6 Conclusion 140 Acknowledgments 140 References 140 7 Parallel Intelligence for CPHS: An ACP Approach 145Xiao Wang, Jing Yang, Xiaoshuang Li, and Fei-Yue Wang 7.1 Background and Motivation 145 7.2 Early Development in China 147 7.3 Key Elements and Framework 149 7.4 Operation and Process 151 7.4.1 Construction of Artificial Systems 152 7.4.2 Computational Experiments in Parallel Intelligent Systems 152 7.4.3 Closed-Loop Optimization Based on Parallel Execution 153 7.5 Applications 153 7.5.1 Parallel Control and Intelligent Control 154 7.5.2 Parallel Robotics and Parallel Manufacturing 156 7.5.3 Parallel Management and Intelligent Organizations 157 7.5.4 Parallel Medicine and Smart Healthcare 158 7.5.5 Parallel Ecology and Parallel Societies 160 7.5.6 Parallel Economic Systems and Social Computing 161 7.5.7 Parallel Military Systems 163 7.5.8 Parallel Cognition and Parallel Philosophy 164 7.6 Conclusion and Prospect 165 References 165 Part II Transportation 171 8 Regularities of Human Operator Behavior and Its Modeling 173Aleksandr V. Efremov 8.1 Introduction 173 8.2 The Key Variables in Man-Machine Systems 174 8.3 Human Responses 177 8.4 Regularities of Man-Machine System in Manual Control 180 8.4.1 Man-Machine System in Single-loop Compensatory System 180 8.4.2 Man-Machine System in Multiloop, Multichannel, and Multimodal Tasks 185 8.4.2.1 Man-Machine System in the Multiloop Tracking Task 185 8.4.2.2 Man-Machine System in the Multichannel Tracking Task 187 8.4.2.3 Man-Machine System in Multimodal Tracking Tasks 188 8.4.2.4 Human Operator Behavior in Pursuit and Preview Tracking Tasks 191 8.5 Mathematical Modeling of Human Operator Behavior in Manual Control Task 194 8.5.1 McRuer's Model for the Pilot Describing Function 194 8.5.1.1 Single-Loop Compensatory Model 194 8.5.1.2 Multiloop and Multimodal Compensatory Model 197 8.5.2 Structural Human Operator Model 197 8.5.3 Pilot Optimal Control Model 199 8.5.4 Pilot Models in Preview and Pursuit Tracking Tasks 201 8.6 Applications of the Man-Machine System Approach 202 8.6.1 Development of Criteria for Flying Qualities and PIO Prediction 203 8.6.1.1 Criteria of FQ and PIO Prediction as a Requirement for the Parameters of the Pilot-Aircraft System 203 8.6.1.2 Calculated Piloting Rating of FQ as the Criteria 205 8.6.2 Interfaces Design 206 8.6.3 Optimization of Control System and Vehicle Dynamics Parameters 210 8.7 Future Research Challenges and Visions 213 8.8 Conclusion 214 References 215 9 Safe Shared Control Between Pilots and Autopilots in the Face of Anomalies 219Emre Eraslan, Yildiray Yildiz, and Anuradha M. Annaswamy 9.1 Introduction 219 9.2 Shared Control Architectures: A Taxonomy 221 9.3 Recent Research Results 222 9.3.1 Autopilot 224 9.3.1.1 Dynamic Model of the Aircraft 224 9.3.1.2 Advanced Autopilot Based on Adaptive Control 225 9.3.1.3 Autopilot Based on Proportional Derivative Control 228 9.3.2 Human Pilot 228 9.3.2.1 Pilot Models in the Absence of Anomaly 228 9.3.2.2 Pilot Models in the Presence of Anomaly 229 9.3.3 Shared Control 230 9.3.3.1 SCA1: A Pilot with a CfM-Based Perception and a Fixed-Gain Autopilot 231 9.3.3.2 SCA2: A Pilot with a CfM-Based Decision-Making and an Advanced Adaptive Autopilot 232 9.3.4 Validation with Human-in-the-Loop Simulations 232 9.3.5 Validation of Shared Control Architecture 1 234 9.3.5.1 Experimental Setup 234 9.3.5.2 Anomaly 235 9.3.5.3 Experimental Procedure 235 9.3.5.4 Details of the Human Subjects 236 9.3.5.5 Pilot-Model Parameters 237 9.3.5.6 Results and Observations 237 9.3.6 Validation of Shared Control Architecture 2 240 9.3.6.1 Experimental Setup 241 9.3.6.2 Anomaly 241 9.3.6.3 Experimental Procedure 242 9.3.6.4 Details of the Human Subjects 243 9.3.6.5 Results and Observations 244 9.4 Summary and Future Work 246 References 247 10 Safe Teleoperation of Connected and Automated Vehicles 251Frank J. Jiang, Jonas Mårtensson, and Karl H. Johansson 10.1 Introduction 251 10.2 Safe Teleoperation 254 10.2.1 The Advent of 5G 258 10.3 CPHS Design Challenges in Safe Teleoperation 259 10.4 Recent Research Advances 261 10.4.1 Enhancing Operator Perception 261 10.4.2 Safe Shared Autonomy 264 10.5 Future Research Challenges 267 10.5.1 Full Utilization of V2X Networks 267 10.5.2 Mixed Autonomy Traffic Modeling 268 10.5.3 5G Experimentation 268 10.6 Conclusions 269 References 270 11 Charging Behavior of Electric Vehicles 273Qing-Shan Jia and Teng Long 11.1 History, Challenges, and Opportunities 274 11.1.1 The History and Status Quo of EVs 274 11.1.2 The Current Challenge 276 11.1.3...