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 xviiAbout the Editors xviiiList of Contributors xixIntroduction xxviiPart I Fundamental Concepts and Methods 11 Human-in-the-Loop Control and Cyber–Physical–Human Systems: Applications and Categorization 3Tariq Samad1.1 Introduction 31.2 Cyber + Physical + Human 41.2.1 Cyberphysical Systems 51.2.2 Physical–Human Systems 61.2.3 Cyber–Human Systems 61.3 Categorizing Human-in-the-Loop Control Systems 61.3.1 Human-in-the-Plant 81.3.2 Human-in-the-Controller 81.3.3 Human–Machine Control Symbiosis 101.3.4 Humans-in-Multiagent-Loops 111.4 A Roadmap for Human-in-the-Loop Control 131.4.1 Self- and Human-Driven Cars on Urban Roads 131.4.2 Climate Change Mitigation and Smart Grids 141.5 Discussion 151.5.1 Other Ways of Classifying Human-in-the-Loop Control 151.5.2 Modeling Human Understanding and Decision-Making 161.5.3 Ethics and CPHS 181.6 Conclusions 19Acknowledgments 19References 202 Human Behavioral Models Using Utility Theory and Prospect Theory 25Anuradha M. Annaswamy and Vineet Jagadeesan Nair2.1 Introduction 252.2 Utility Theory 262.2.1 An Example 272.3 Prospect Theory 272.3.1 An Example: CPT Modeling for SRS 302.3.1.1 Detection of CPT Effects via Lotteries 322.3.2 Theoretical Implications of CPT 332.3.2.1 Implication I: Fourfold Pattern of Risk Attitudes 342.3.2.2 Implication II: Strong Risk Aversion Over Mixed Prospects 362.3.2.3 Implication III: Effects of Self-Reference 372.4 Summary and Conclusions 38Acknowledgments 39References 393 Social Diffusion Dynamics in Cyber–Physical–Human Systems 43Lorenzo Zino and Ming Cao3.1 Introduction 433.2 General Formalism for Social Diffusion in CPHS 453.2.1 Complex and Multiplex Networks 453.2.2 General Framework for Social Diffusion 463.2.3 Main Theoretical Approaches 483.3 Modeling Decision-Making 493.3.1 Pairwise Interaction Models 493.3.2 Linear Threshold Models 523.3.3 Game-Theoretic Models 533.4 Dynamics in CPHS 553.4.1 Social Diffusion in Multiplex Networks 563.4.2 Co-Evolutionary Social Dynamics 583.5 Ongoing Efforts Toward Controlling Social Diffusion and Future Challenges 62Acknowledgments 63References 634 Opportunities and Threats of Interactions Between Humans and Cyber–Physical Systems – Integration and Inclusion Approaches for Cphs 71Frédéric Vanderhaegen and Victor Díaz Benito Jiménez4.1 CPHS and Shared Control 724.2 “Tailor-made” Principles for Human–CPS Integration 734.3 “All-in-one” based Principles for Human–CPS Inclusion 744.4 Dissonances, Opportunities, and Threats in a CPHS 764.5 Examples of Opportunities and Threats 794.6 Conclusions 85References 865 Enabling Human-Aware Autonomy Through Cognitive Modeling and Feedback Control 91Neera Jain, Tahira Reid, Kumar Akash, Madeleine Yuh, and Jacob Hunter5.1 Introduction 915.1.1 Important Cognitive Factors in HAI 925.1.2 Challenges with Existing CPHS Methods 935.1.3 How to Read This Chapter 955.2 Cognitive Modeling 955.2.1 Modeling Considerations 955.2.2 Cognitive Architectures 975.2.3 Computational Cognitive Models 985.2.3.1 ARMAV and Deterministic Linear Models 995.2.3.2 Dynamic Bayesian Models 995.2.3.3 Decision Analytical Models 1005.2.3.4 POMDP Models 1025.3 Study Design and Data Collection 1035.3.1 Frame Research Questions and Identify Variables 1045.3.2 Formulate Hypotheses or Determine the Data Needed 1055.3.2.1 Hypothesis Testing Approach 1055.3.2.2 Model Training Approach 1055.3.3 Design Experiment and/or Study Scenario 1075.3.3.1 Hypothesis Testing Approach 1075.3.3.2 Model Training Approach 1075.3.4 Conduct Pilot Studies and Get Initial Feedback; Do Preliminary Analysis 1085.3.5 A Note about Institutional Review Boards and Recruiting Participants 1095.4 Cognitive Feedback Control 1095.4.1 Considerations for Feedback Control 1105.4.2 Approaches 1115.4.2.1 Heuristics-Based Planning 1115.4.2.2 Measurement-Based Feedback 1125.4.2.3 Goal-Oriented Feedback 1125.4.2.4 Case Study 1125.4.3 Evaluation Methods 1135.5 Summary and Opportunities for Further Investigation 1135.5.1 Model Generalizability and Adaptability 1145.5.2 Measurement of Cognitive States 1145.5.3 Human Subject Study Design 114References 1156 Shared Control with Human Trust and Workload Models 125Murat Cubuktepe, Nils Jansen, and Ufuk Topcu6.1 Introduction 1256.1.1 Review of Shared Control Methods 1266.1.2 Contribution and Approach 1276.1.3 Review of IRL Methods Under Partial Information 1286.1.3.1 Organization 1296.2 Preliminaries 1296.2.1 Markov Decision Processes 1296.2.2 Partially Observable Markov Decision Processes 1306.2.3 Specifications 1306.3 Conceptual Description of Shared Control 1316.4 Synthesis of the Autonomy Protocol 1326.4.1 Strategy Blending 1326.4.2 Solution to the Shared Control Synthesis Problem 1336.4.2.1 Nonlinear Programming Formulation for POMDPs 1336.4.2.2 Strategy Repair Using Sequential Convex Programming 1346.4.3 Sequential Convex Programming Formulation 1356.4.4 Linearizing Nonconvex Problem 1356.4.4.1 Linearizing Nonconvex Constraints and Adding Slack Variables 1356.4.4.2 Trust Region Constraints 1366.4.4.3 Complete Algorithm 1366.4.4.4 Additional Specifications 1366.4.4.5 Additional Measures 1376.5 Numerical Examples 1376.5.1 Modeling Robot Dynamics as POMDPs 1386.5.2 Generating Human Demonstrations 1386.5.3 Learning a Human Strategy 1396.5.4 Task Specification 1396.5.5 Results 1406.6 Conclusion 140Acknowledgments 140References 1407 Parallel Intelligence for CPHS: An ACP Approach 145Xiao Wang, Jing Yang, Xiaoshuang Li, and Fei-Yue Wang7.1 Background and Motivation 1457.2 Early Development in China 1477.3 Key Elements and Framework 1497.4 Operation and Process 1517.4.1 Construction of Artificial Systems 1527.4.2 Computational Experiments in Parallel Intelligent Systems 1527.4.3 Closed-Loop Optimization Based on Parallel Execution 1537.5 Applications 1537.5.1 Parallel Control and Intelligent Control 1547.5.2 Parallel Robotics and Parallel Manufacturing 1567.5.3 Parallel Management and Intelligent Organizations 1577.5.4 Parallel Medicine and Smart Healthcare 1587.5.5 Parallel Ecology and Parallel Societies 1607.5.6 Parallel Economic Systems and Social Computing 1617.5.7 Parallel Military Systems 1637.5.8 Parallel Cognition and Parallel Philosophy 1647.6 Conclusion and Prospect 165References 165Part II Transportation 1718 Regularities of Human Operator Behavior and Its Modeling 173Aleksandr V. Efremov8.1 Introduction 1738.2 The Key Variables in Man–Machine Systems 1748.3 Human Responses 1778.4 Regularities of Man–Machine System in Manual Control 1808.4.1 Man–Machine System in Single-loop Compensatory System 1808.4.2 Man–Machine System in Multiloop, Multichannel, and Multimodal Tasks 1858.4.2.1 Man–Machine System in the Multiloop Tracking Task 1858.4.2.2 Man–Machine System in the Multichannel Tracking Task 1878.4.2.3 Man–Machine System in Multimodal Tracking Tasks 1888.4.2.4 Human Operator Behavior in Pursuit and Preview Tracking Tasks 1918.5 Mathematical Modeling of Human Operator Behavior in Manual Control Task 1948.5.1 McRuer’s Model for the Pilot Describing Function 1948.5.1.1 Single-Loop Compensatory Model 1948.5.1.2 Multiloop and Multimodal Compensatory Model 1978.5.2 Structural Human Operator Model 1978.5.3 Pilot Optimal Control Model 1998.5.4 Pilot Models in Preview and Pursuit Tracking Tasks 2018.6 Applications of the Man–Machine System Approach 2028.6.1 Development of Criteria for Flying Qualities and PIO Prediction 2038.6.1.1 Criteria of FQ and PIO Prediction as a Requirement for the Parameters of the Pilot-Aircraft System 2038.6.1.2 Calculated Piloting Rating of FQ as the Criteria 2058.6.2 Interfaces Design 2068.6.3 Optimization of Control System and Vehicle Dynamics Parameters 2108.7 Future Research Challenges and Visions 2138.8 Conclusion 214References 2159 Safe Shared Control Between Pilots and Autopilots in the Face of Anomalies 219Emre Eraslan, Yildiray Yildiz, and Anuradha M. Annaswamy9.1 Introduction 2199.2 Shared Control Architectures: A Taxonomy 2219.3 Recent Research Results 2229.3.1 Autopilot 2249.3.1.1 Dynamic Model of the Aircraft 2249.3.1.2 Advanced Autopilot Based on Adaptive Control 2259.3.1.3 Autopilot Based on Proportional Derivative Control 2289.3.2 Human Pilot 2289.3.2.1 Pilot Models in the Absence of Anomaly 2289.3.2.2 Pilot Models in the Presence of Anomaly 2299.3.3 Shared Control 2309.3.3.1 SCA1: A Pilot with a CfM-Based Perception and a Fixed-Gain Autopilot 2319.3.3.2 SCA2: A Pilot with a CfM-Based Decision-Making and an Advanced Adaptive Autopilot 2329.3.4 Validation with Human-in-the-Loop Simulations 2329.3.5 Validation of Shared Control Architecture 1 2349.3.5.1 Experimental Setup 2349.3.5.2 Anomaly 2359.3.5.3 Experimental Procedure 2359.3.5.4 Details of the Human Subjects 2369.3.5.5 Pilot-Model Parameters 2379.3.5.6 Results and Observations 2379.3.6 Validation of Shared Control Architecture 2 2409.3.6.1 Experimental Setup 2419.3.6.2 Anomaly 2419.3.6.3 Experimental Procedure 2429.3.6.4 Details of the Human Subjects 2439.3.6.5 Results and Observations 2449.4 Summary and Future Work 246References 24710 Safe Teleoperation of Connected and Automated Vehicles 251Frank J. Jiang, Jonas Mårtensson, and Karl H. Johansson10.1 Introduction 25110.2 Safe Teleoperation 25410.2.1 The Advent of 5G 25810.3 CPHS Design Challenges in Safe Teleoperation 25910.4 Recent Research Advances 26110.4.1 Enhancing Operator Perception 26110.4.2 Safe Shared Autonomy 26410.5 Future Research Challenges 26710.5.1 Full Utilization of V2X Networks 26710.5.2 Mixed Autonomy Traffic Modeling 26810.5.3 5G Experimentation 26810.6 Conclusions 269References 27011 Charging Behavior of Electric Vehicles 273Qing-Shan Jia and Teng Long11.1 History, Challenges, and Opportunities 27411.1.1 The History and Status Quo of EVs 27411.1.2 The Current Challenge 27611.1.3 The Opportunities 27711.2 Data Sets and Problem Modeling 27811.2.1 Data Sets of EV Charging Behavior 27811.2.1.1 Trend Data Sets 27911.2.1.2 Driving Data Sets 27911.2.1.3 Battery Data Sets 27911.2.1.4 Charging Data Sets 27911.2.2 Problem Modeling 28111.3 Control and Optimization Methods 28411.3.1 The Difficulty of the Control and Optimization 28411.3.2 Charging Location Selection and Routing Optimization 28511.3.3 Charging Process Control 28611.3.4 Control and Optimization Framework 28711.3.4.1 Centralized Optimization 28711.3.4.2 Decentralized Optimization 28811.3.4.3 Hierarchical Optimization 28811.3.5 The Impact of Human Behaviors 28911.4 Conclusion and Discussion 289References 290Part III Robotics 29912 Trust-Triggered Robot–Human Handovers Using Kinematic Redundancy for Collaborative Assembly in Flexible Manufacturing 301S. M. Mizanoor Rahman, Behzad Sadrfaridpour, Ian D. Walker, and Yue Wang12.1 Introduction 30112.2 The Task Context and the Handover 30312.3 The Underlying Trust Model 30412.4 Trust-Based Handover Motion Planning Algorithm 30512.4.1 The Overall Motion Planning Strategy 30512.4.2 Manipulator Kinematics and Kinetics Models 30512.4.3 Dynamic Impact Ellipsoid 30612.4.4 The Novel Motion Control Approach 30712.4.5 Illustration of the Novel Algorithm 30812.5 Development of the Experimental Settings 31012.5.1 Experimental Setup 31012.5.1.1 Type I: Center Console Assembly 31012.5.1.2 Type II: Hose Assembly 31112.5.2 Real-Time Measurement and Display of Trust 31112.5.2.1 Type I: Center Console Assembly 31112.5.2.2 Type II: Hose Assembly 31312.5.2.3 Trust Computation 31312.5.3 Plans to Execute the Trust-Triggered Handover Strategy 31412.5.3.1 Type I Assembly 31412.5.3.2 Type II Assembly 31412.6 Evaluation of the Motion Planning Algorithm 31512.6.1 Objective 31512.6.2 Experiment Design 31512.6.3 Evaluation Scheme 31512.6.4 Subjects 31612.6.5 Experimental Procedures 31612.6.5.1 Type I Assembly 31712.6.5.2 Type II Assembly 31712.7 Results and Analyses, Type I Assembly 31812.8 Results and Analyses, Type II Assembly 32212.9 Conclusions and Future Work 323Acknowledgment 324References 32413 Fusing Electrical Stimulation and Wearable Robots with Humans to Restore and Enhance Mobility 329Thomas Schauer, Eduard Fosch-Villaronga, and Juan C. Moreno13.1 Introduction 32913.1.1 Functional Electrical Stimulation 33013.1.2 Spinal Cord Stimulation 33113.1.3 Wearable Robotics (WR) 33213.1.4 Fusing FES/SCS and Wearable Robotics 33413.2 Control Challenges 33513.2.1 Feedback Approaches to Promote Volition 33613.2.2 Principles of Assist-as-Needed 33613.2.3 Tracking Control Problem Formulation 33613.2.4 Co-operative Control Strategies 33713.2.5 EMG- and MMG-Based Assessment of Muscle Activation 34413.3 Examples 34513.3.1 A Hybrid Robotic System for Arm Training of Stroke Survivors 34513.3.2 First Certified Hybrid Robotic Exoskeleton for Gait Rehabilitation Settings 34713.3.3 Body Weight-Supported Robotic Gait Training with tSCS 34813.3.4 Modular FES and Wearable Robots to Customize Hybrid Solutions 34813.4 Transfer into Daily Practice: Integrating Ethical, Legal, and Societal Aspects into the Design 35013.5 Summary and Outlook 352Acknowledgments 353Acronyms 353References 35414 Contemporary Issues and Advances in Human–Robot Collaborations 365Takeshi Hatanaka, Junya Yamauchi, Masayuki Fujita, and Hiroyuki Handa14.1 Overview of Human–Robot Collaborations 36514.1.1 Task Architecture 36614.1.2 Human–Robot Team Formation 36814.1.3 Human Modeling: Control and Decision 36914.1.4 Human Modeling: Other Human Factors 37114.1.5 Industrial Perspective 37214.1.6 What Is in This Chapter 37514.2 Passivity-Based Human-Enabled Multirobot Navigation 37614.2.1 Architecture Design 37714.2.2 Human Passivity Analysis 37914.2.3 Human Workload Analysis 38114.3 Operation Support with Variable Autonomy via Gaussian Process 38314.3.1 Design of the Operation Support System with Variable Autonomy 38514.3.2 User Study 38814.3.2.1 Operational Verification 38814.3.2.2 Usability Test 39014.4 Summary 391Acknowledgments 393References 393Part IV Healthcare 40115 Overview and Perspectives on the Assessment and Mitigation of Cognitive Fatigue in Operational Settings 403Mike Salomone, Michel Audiffren, and Bruno Berberian15.1 Introduction 40315.2 Cognitive Fatigue 40415.2.1 Definition 40415.2.2 Origin of Cognitive Fatigue 40415.2.3 Effects on Adaptive Capacities 40615.3 Cyber–Physical System and Cognitive Fatigue: More Automation Does Not Imply Less Cognitive Fatigue 40615.4 Assessing Cognitive Fatigue 40915.4.1 Subjective Measures 40915.4.2 Behavioral Measures 41015.4.3 Physiological Measurements 41015.5 Limitations and Benefits of These Measures 41215.6 Current and Future Solutions and Countermeasures 41215.6.1 Physiological Computing: Toward Real-Time Detection and Adaptation 41215.7 System Design and Explainability 41415.8 Future Challenges 41515.8.1 Generalizing the Results Observed in the Laboratory to Ecological Situations 41515.8.2 Determining the Specificity of Cognitive Fatigue 41515.8.3 Recovering from Cognitive Fatigue 41715.9 Conclusion 418References 41916 Epidemics Spread Over Networks: Influence of Infrastructure and Opinions 429Baike She, Sebin Gracy, Shreyas Sundaram, Henrik Sandberg, Karl H. Johansson, andPhilipE.Paré16.1 Introduction 42916.1.1 Infectious Diseases 42916.1.2 Modeling Epidemic Spreading Processes 43016.1.3 Susceptible–Infected–Susceptible (SIS) Compartmental Models 43116.2 Epidemics on Networks 43216.2.1 Motivation 43216.2.2 Modeling Epidemics over Networks 43316.2.3 Networked Susceptible–Infected–Susceptible Epidemic Models 43416.3 Epidemics and Cyber–Physical–Human Systems 43616.3.1 Epidemic and Opinion Spreading Processes 43716.3.2 Epidemic and Infrastructure 43816.4 Recent Research Advances 43916.4.1 Notation 43916.4.2 Epidemic and Opinion Spreading Processes 44016.4.2.1 Opinions Over Networks with Both Cooperative and Antagonistic Interactions 44016.4.2.2 Coupled Epidemic and Opinion Dynamics 44116.4.2.3 Opinion-Dependent Reproduction Number 44316.4.2.4 Simulations 44416.4.3 Epidemic Spreading with Shared Resources 44516.4.3.1 The Multi-Virus SIWS Model 44516.4.3.2 Problem Statements 44716.4.3.3 Analysis of the Eradicated State of a Virus 44816.4.3.4 Persistence of a Virus 44916.4.3.5 Simulations 44916.5 Future Research Challenges and Visions 450References 45117 Digital Twins and Automation of Care in the Intensive Care Unit 457J. Geoffrey Chase, Cong Zhou, Jennifer L. Knopp, Knut Moeller, Balázs Benyo, Thomas Desaive, Jennifer H. K. Wong, Sanna Malinen, Katharina Naswall, Geoffrey M. Shaw, Bernard Lambermont, and Yeong S. Chiew17.1 Introduction 45717.1.1 Economic Context 45817.1.2 Healthcare Context 45917.1.3 Technology Context 46017.1.4 Overall Problem and Need 46017.2 Digital Twins and CPHS 46117.2.1 Digital Twin/Virtual Patient Definition 46117.2.2 Requirements in an ICU Context 46317.2.3 Digital Twin Models in Key Areas of ICU Care and Relative to Requirements 46417.2.4 Review of Digital Twins in Automation of ICU Care 46617.2.5 Summary 46717.3 Role of Social-Behavioral Sciences 46717.3.1 Introduction 46717.3.2 Barriers to Innovation Adoption 46717.3.3 Ergonomics and Codesign 46817.3.4 Summary (Key Takeaways) 46917.4 Future Research Challenges and Visions 47017.4.1 Technology Vision of the Future of CPHS in ICU Care 47017.4.2 Social-Behavioral Sciences Vision of the Future of CPHS in ICU Care 47117.4.3 Joint Vision of the Future and Challenges to Overcome 47317.5 Conclusions 473References 474Part V Sociotechnical Systems 49118 Online Attention Dynamics in Social Media 493Maria Castaldo, Paolo Frasca, and Tommaso Venturini18.1 Introduction to Attention Economy and Attention Dynamics 49318.2 Online Attention Dynamics 49418.2.1 Collective Attention Is Limited 49418.2.2 Skewed Attention Distribution 49518.2.3 The Role of Novelty 49618.2.4 The Role of Popularity 49618.2.5 Individual Activity Is Bursty 49918.2.6 Recommendation Systems Are the Main Gateways for Information 50018.2.7 Change Is the Only Constant 50018.3 The New Challenge: Understanding Recommendation Systems Effect in Attention Dynamics 50118.3.1 Model Description 50218.3.2 Results and Discussion 50318.4 Conclusion 505Acknowledgments 505References 50519 Cyber–Physical–Social Systems for Smart City 511Gang Xiong, Noreen Anwar, Peijun Ye, Xiaoyu Chen, Hongxia Zhao, Yisheng Lv, Fenghua Zhu, Hongxin Zhang, Xu Zhou, and Ryan W. Liu19.1 Introduction 51119.2 Social Community and Smart Cities 51319.2.1 Smart Infrastructure 51319.2.2 Smart Energy 51519.2.3 Smart Transportation 51519.2.4 Smart Healthcare 51719.3 CPSS Concepts, Tools, and Techniques 51819.3.1 CPSS Concepts 51819.3.2 CPSS Tools 51919.3.3 CPSS Techniques 52019.3.3.1 IoT in Smart Cities 52019.3.3.2 Big Data in Smart Cities 52519.4 Recent Research Advances 52819.4.1 Recent Research Advances of CASIA 52819.4.2 Recent Research in European Union 53119.4.3 Future Research Challenges and Visions 53319.5 Conclusions 537Acknowledgments 538References 538Part VI Concluding Remarks 54320 Conclusion and Perspectives 545Anuradha M. Annaswamy, Pramod P. Khargonekar, Françoise Lamnabhi-Lagarrigue, and Sarah K. Spurgeon20.1 Benefits to Humankind: Synthesis of the Chapters and their Open Directions 54520.2 Selected Areas for Current and Future Development in CPHS 54720.2.1 Driver Modeling for the Design of Advanced Driver Assistance Systems 54720.2.2 Cognitive Cyber–Physical Systems and CPHS 54720.2.3 Emotion–Cognition Interactions 54820.3 Ethical and Social Concerns: Few Directions 54920.3.1 Frameworks for Ethics 55020.3.2 Technical Approaches 55020.4 Afterword 551References 551Index 555