Constrained Control and Machine Learning : Emerging Methodologies and Applications
Language: English
Published by Springer, 2026
- Hardcover
- New
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Druck auf Anfrage Neuware - Printed after ordering - 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.…
Seller Inventory # 9783032027085
- Title
- Constrained Control and Machine Learning : Emerging Methodologies and Applications
- Author
- Giancarlo Fortino
- Publisher
- Springer
- Publication year
- 2026
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 303202708X
- ISBN 13
- 9783032027085
- Item weight
- 645 grams
- Dimensions
- 241x160x23 mm
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.
"Synopsis" may belong to another edition of this title.
About the Author
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 Internet of Things series and AE of premier int'l journals such as IEEE TASE (senior editor), IEEE TAFFC-CS, IEEE THMS, IEEE T-AI, IEEE IoTJ, IEEE SJ, IEEE JBHI, IEEE SMCM, IEEE OJEMB, IEEE OJCS, Information Fusion, EAAI, etc. He chaired many int’l workshops and conferences (130+), was involved in a huge number of int’l conferences/workshops (700+) as IPC member, is/was guest-editor of many special issues (80+). He is cofounder and CEO of SenSysCal S.r.l., a Unical spinoff focused on innovative IoT systems, and recently cofounder and vice-CEO of the spin-off Bigtech S.r.l, focused on big data, AI and IoT technologies. Fortino is currently AVP of the Cybernetics area of the IEEE SMCS and former member of the IEEE SMCS BoG and former chair of the IEEE SMCS Italian Chapter.
Dr. Walter Lucia is an Associate Professor at the Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Canada. He received the M.Sc. degree in automation engineering (2011) and the Ph.D. degree in Systems and Computer Engineering (2015) from the University of Calabria, Italy. In 2013, he was a visiting research scholar in the ECE Department at Northeastern University (USA), and in 2015, a visiting postdoctoral researcher in the ECE Department at Carnegie Mellon University (USA). In 2016, Dr. Lucia joined Concordia University as a tenure-track Assistant Professor, where, in 2021, he was promoted to the rank of Associate Professor with tenure. Dr. Lucia current research interests include control of unmanned vehicles, predictive control, fault-tolerant control, and secure and resilient control of cyber-physical systems. Dr. Lucia is currently an Associate Editor for IEEE Control Systems Letters, Control System Society - Conference Editorial Board and IEEE Systems Journal. Moreover, he serves as the Chair of the IEEE Montreal Chapters of Systems, Man and Cybernetics, and IEEE Control Systems.
MengChu Zhou received his B.S. degree in Control Engineering from Nanjing University of Science and Technology, Nanjing, China in 1983, M.S. degree in Automatic Control from Beijing Institute of Technology, Beijing, China in 1986, and Ph. D. degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, NY in 1990. He joined the Department of Electrical and Computer Engineering, New Jersey Institute of Technology in 1990, and is now a Distinguished Professor. His interests are in intelligent automation/transportation, robotics, Petri nets, Internet of Things, edge/cloud computing, and big data analytics. He has over 1300 publications including 17 books, over 900 journal papers including over 700 IEEE Transactions papers, 31 patents and 32 book-chapters. He is a recipient of Excellence in Research Prize and Medal from NJIT, Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, and Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man, and Cybernetics Society, and Edison Patent Award from the Research & Development Council of New Jersey. He is a life member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is Fellow of IEEE, International Federation of Automatic Control (IFAC), American Association for the Advancement of Science (AAAS), Chinese Association of Automation (CAA) and National Academy of Inventors (NAI).
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