Learning-Enabled Autonomous Systems
Language: English
Published by CRC Press, 2026
- Hardcover
- New
Condition: New
£ 108.56
Quantity: Over 20 available
Add to basketItem description from seller
Dr. Jianglin Lan is a Lecturer in Autonomous Systems and Leverhulme Early Career Fellow at the James Watt School of Engineering, University of Glasgow, and an Honorary Research Fellow at Imperial College London. He received his PhD in Control and Intelligen.
Seller Inventory # 2940184183
- Title
- Learning-Enabled Autonomous Systems
- Author
- Jianglin Lan
- Publisher
- CRC Press
- Publication year
- 2026
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1041302770
- ISBN 13
- 9781041302773
This book examines how to design intelligent systems that are not only adaptive but also safe and reliable. This book bridges the gap between traditional control theory and modern data-driven learning, presenting a unified framework for creating autonomous systems capable of robust decision-making in uncertain and dynamic environments.
Learning-Enabled Autonomous Systems: Control, Verification, and Monitoring stands out as a unique resource for designing trustworthy autonomous systems. It introduces data-driven control methods that allow systems to learn from real-world data, enabling adaptability and intelligence without sacrificing mathematical rigor. The book explores advanced control strategies for nonlinear systems, ensuring computational practicality for real-world applications. It also provides innovative techniques for verifying neural network controllers and safeguarding system performance through runtime monitoring frameworks. By uniting control theory, machine learning, and systems verification, this book offers a holistic approach to creating systems that are not only intelligent but also resilient, transparent, and dependable. It includes case studies, algorithmic insights, and design guidelines that connect theoretical principles to hands-on engineering practice.
This book is tailored for graduate students, researchers, and practitioners in control systems, robotics, artificial intelligence, and systems engineering. It is ideal for those seeking to deepen their understanding of learning-enabled control systems, whether for academic study or real-world application.
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About the Author
"About the title" may belong to another edition of this title.
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