Assuring Safe Operation of Robotic Systems under Uncertainty : Control and Learning Methods

Li, Cong/ Wang, Yongchao/ Liu, Fangzhou

CRC Press（2025/11発売）

• 製本 Hardcover:ハードカバー版／ページ数 120 p.
• 言語 ENG
• 商品コード 9781041141204

Full Description

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.

Contents

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