Constrained Control and Machine Learning
Springer International Publishing (Verlag)
978-3-032-02708-5 (ISBN)
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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.
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.
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.
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).
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.
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.
| Erscheinungsdatum | 29.11.2025 |
|---|---|
| Reihe/Serie | Internet of Things |
| Zusatzinfo | X, 140 p. 50 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Technik ► Elektrotechnik / Energietechnik |
| Technik ► Nachrichtentechnik | |
| Schlagworte | Autonomous Vehicles • Constrained Control • Data-driven modeling • Deep reinforcement learning applications • distributed Architectures • Healthcare Applications • machine learning • traffic control |
| ISBN-10 | 3-032-02708-X / 303202708X |
| ISBN-13 | 978-3-032-02708-5 / 9783032027085 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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