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Details for: Li C. Assuring Safe Operation of Robotic Systems under Uncertainty...2025

li c assuring safe operation robotic systems under uncertainty 2025

Type:: E-books
Files:: 1
Size:: 8.8 MB
Uploaded On:: Nov. 29, 2025, 10:45 a.m.
Added By:: andryold1
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Leechers:: 14
Info Hash:: 5B687D5B13E94E01DB60F3CBF3C76BAAE2A89E4E

Textbook in PDF format 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

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Li C. Assuring Safe Operation of Robotic Systems under Uncertainty...2025.pdf 8.8 MB

Details for: Li C. Assuring Safe Operation of Robotic Systems under Uncertainty...2025

li c assuring safe operation robotic systems under uncertainty 2025

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