This book 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.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Postgraduate and Professional Reference
Maße
Höhe: 234 mm
Breite: 156 mm
ISBN-13
978-1-041-14120-4 (9781041141204)
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Schweitzer Klassifikation
Cong Li received the Ph.D. degree 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 received the B.S. degree from Zhejiang University, China, in 2011, the M.S. degree in mechanical engineering from the PLA University of Science and Technology, China, in 2014, and the Ph.D. degree in system and control from the Politecnico di Milano, Italy, 2018. He is currently an associate professor at the College of Intelligence Science and Technology, National University of Defense Technology, China.
1 Introduction to Safety under Uncertainty Section I Set-Theoretic Methods 2 Guaranteed Safety and Performance via Concurrent Learning 3 Provable Robust Safety Through Barrier Lyapunov Function 4 Safe Navigation via Integrated Perception and Control Section II Reinforcement Learning Approaches 5 Constrained Optimal Control Through Risk-Sensitive RL 6 Safe Approximate Optimal Control via Filtered RL 7 Time-Delayed Data Informed RL for Optimal Tracking Control
