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.

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Yes, you can access Assuring Safe Operation of Robotic Systems under Uncertainty by Cong Li,Yongchao Wang,Fangzhou Liu,Xinglong Zhang in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Automotive Transportation & Engineering. We have over one million books available in our catalogue for you to explore.