Assuring Safe Operation of Robotic Systems under Uncertainty

Control and Learning Methods
Author(s): Cong Li, Yongchao Wang, Fangzhou Liu, Xinglong Zhang
Format: Hardback
Publisher: Taylor & Francis Ltd, United Kingdom
Imprint: CRC Press
ISBN-13: 9781041141204, 978-1041141204

Synopsis

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 methodsspecifically, the barrier Lyapunov function and the control barrier functionto 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.