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Description
This book addresses the use of constrained control and machine learning approaches within data-driven settings in the field of autonomous robots for Industry 5.0 and Intelligent Transportation Systems. The primary aim of the book is to highlight the strict connection between constrained control and machine learning when tackling real-like phenomena in terms of a data-driven framework. The book shows how constrained control techniques and machine learning approaches can be adequately combined to derive novel and more efficient hybrid control architectures for data-driven based scenarios. To this end, several control problems ranging from planning and formation of autonomous multi-vehicles, routing decisions in urban road networks, freeway traffic modeling, to autonomous robotics in healthcare, are considered to highlight the capability of the data-driven approach to combine techniques coming from different research domains. The book is mainly devoted to researchers that, starting from a solid expertise on the constrained control and/or machine learning tools, would improve their ability to jointly use these technicalities in the data-driven setting.
- Addresses use of constrained control and machine learning within data-driven settings;
- Focuses on applications in autonomous robots for Industry 5.0 and intelligent transportation systems;
- Shows how combined constrained control and ML techniques can create efficient hybrid control architectures.
Introduction.- Freeway Traffic Modelling.- Safe Distributed Set-Based Planning in Autonomous Vehicles.- The role of human motion models in learning-based social navigation systems.- Autonomous Vehicle Platoons in Urban Road Networks.- Delay Injection Attacks against Haptic Shared Control Steering Systems.- Application of Deep Reinforcement Learning for Traffic Control of Road Intersection with Autonomous Vehicles.- A Feedback-Linearized Model Predictive Control Strategy for Constrained Wheeled Mobile Robots.- A resilient distributed architecture for UAV leader-follower formations.- Automation and Robotics in Healthcare: Transforming Hospital Material Handling.- Multi-Vehicle Localization by Distributed Moving Horizon Estimation over Sensor Networks.- Conclusion.
Dr. Giuseppe Franzè is a Full Professor at the DIMEG department of the University of Calabria (Italy). Dr. Franze received the Laurea degree in Computer Engineering in 1994 and the Ph.D. degree in Systems Engineering in 1999 from the University of Calabria, Italy. He authored or co-authored of more than 220 research papers in archival journals, book chapters and international conference proceedings. His current research interests include constrained predictive control, nonlinear systems, networked control systems, control under constraints and control reconfiguration for fault tolerant systems, resilient control for cyber-physical systems. In November-December 2019, he was a visiting professor at Concordia University (Canada) at the CIISE Department. Since 2019 he is Senior Member of IEEE. He is a co-recipient of the Best Paper Award at the IEEE-CoDIT 2019 Conference, Paris, France and at the IEEE-CoDIT 2024 Conference, Valetta, Malta. He currently serves as Associate Editor for IEEE/CAA Journal of Automatica Sinica (JAS), IEEE Transactions on Automation and Science Engineering and IEEE Transactions on Intelligent Vehicles. He is the Guest Editor of the Special Issue Resilient Control in LargeScale Networked Cyber-Physical Systems IEEE/CAA Journal of Automatica Sinica (JAS), 2020. From January 2018 to March 2022, he was the Graduate Program Director of the Master Degree in Automation Engineering at the DIMES department, University of Calabria. Since September 2022, he is a member of the IFAC Technical committee TC 6.4. Fault Detection, Supervision & Safety of Techn. Processes-SAFEPROCESS. Moreover, he is a member of the working group Safety and Security of Cyberphysical Systems of the IFAC Technical committee TC 6.4.
Dr. Giancarlo Fortino is a Full Professor of Computer Engineering at the Dept of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy. He received a PhD in Computer Engineering from Unical in 2000. He is also distinguished professor at Wuhan University of Technology and Huazhong Agricultural University (China), high-end expert at HUST, NIST, ECJTU, CUST (China), senior research fellow at the Italian ICAR-CNR Institute, CAS PIFI foreign scientist at SIAT Shenzhen, and Distinguished Lecturer for IEEE Sensors Council, SMCS and IoT TC. He was also visiting researcher at ICSI, Berkeley (USA), in 1997 and 1999 and visiting professor at Queensland University of technology in 2009. At Unical, he is the Rector s delegate to Int l relations, the chair of the PhD School in ICT, the director of the Postgraduate Master course in AI-driven Radiomics, and the director of the SPEME lab as well as co-chair of Joint labs on IoT established between Unical and WUT, SMU and HZAU Chinese universities, respectively. Fortino is currently the scientific responsible of the Digital Health group of the Italian CINI National Laboratory at Unical. He is Highly Cited Researcher 2020-2024 in Computer Science by Clarivate. He had 25+ highly cited papers in WoS, and h-index=86 with 30000+ citations in Google Scholar. His research interests include wearable computing systems, e-Health, Internet of Things, and agent-based computing. He is author of 750+ papers in int l journals, conferences and books. He is (founding) series editor of IEEE Press Book Series on Human-Machine Systems and EiC of Springer
