Assuring Safe Operation of Robotic Systems under Uncertainty
Language: English
Published by Taylor and Francis Ltd, GB, 2025
- Hardcover
- New
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
AbeBooks seller since June 11, 2025
Condition: New
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Add to basketItem description from seller
Assuring Safe Operation of Robotic Systems under Uncertainty: Control and Learning Methods applies set-theoretic and reinforcement learning approaches to formulate, analyze, and solve the challenge of ensuring safe operation of robotic systems in an uncertain environment.The authors adopt learning-supported, set-theoretic methods-specifically, the barrier Lyapunov function and the control barrier function-to achieve desirable robust safety with guaranteed performance in continuous-time nonlinear control applications. They also combine reinforcement learning with control theory to ensure safe learning and optimization. The reinforcement learning-based optimization framework incorporates safety and robustness guarantees by applying theoretical analysis tools from the field of control.This book will be of interest to researchers, engineers, and students specializing in robot planning and control.
Seller Inventory # LU-9781041141204
- Title
- Assuring Safe Operation of Robotic Systems under Uncertainty
- Author
- Cong Li, Yongchao Wang, Fangzhou Liu, Xinglong Zhang
- Publisher
- Taylor and Francis Ltd, GB
- Publication year
- 2025
- Condition
- New
- Binding
- Hardback
- Language
- English
- ISBN 10
- 1041141203
- ISBN 13
- 9781041141204
- Item weight
- 320 grams
Assuring Safe Operation of Robotic Systems under Uncertainty: Control and Learning Methods applies set-theoretic and reinforcement learning approaches to formulate, analyze, and solve the challenge of ensuring safe operation of robotic systems in an uncertain environment.
The authors adopt learning-supported, set-theoretic methods―specifically, the barrier Lyapunov function and the control barrier function―to achieve desirable robust safety with guaranteed performance in continuous-time nonlinear control applications. They also combine reinforcement learning with control theory to ensure safe learning and optimization. The reinforcement learning-based optimization framework incorporates safety and robustness guarantees by applying theoretical analysis tools from the field of control.
This book will be of interest to researchers, engineers, and students specializing in robot planning and control.
"Synopsis" may belong to another edition of this title.
About the Author
Cong Li earned a PhD from the Chair of Automatic Control Engineering, Technical University of Munich, Germany in 2022. He was also a research associate at the Chair of Automatic Control Engineering, Technical University of Munich.
Yongchao Wang is at the Xi'an Research Institution of Hi-Technology and a professor at the School of Aerospace Science and Technology, Xidian University, Xi'an, China. He was at the Chair of Automatic Control Engineering, Technical University of Munich, Germany.
Fangzhou Liu received the Doktor-Ingenieur degree in electrical engineering from the Technical University of Munich, Germany in 2019. He was a lecturer and a research fellow at the Chair of Automatic Control Engineering, Technical University of Munich, Germany. He is now a full professor at the School of Astronautics, Harbin Institute of Technology, Harbin, China.
Xinglong Zhang earned a BE in mechanical enineering from Zhejiang University, Hangzhou, China in 2011 and a PhD in system and control from the Politecnico di Milano, Italy, 2018. He is presently an associate professor at the College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China. His research interests include Koopman operators, learning-based model predictive control, reinforcement learning, and approximate dynamic programming, and their applications in automotive systems.
"About the title" may belong to another edition of this title.
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