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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Assuring Safe Operation of Robotic Systems Under Uncertainty: Control and Learning Methods

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

Li, Cong
Wang, Yongchao
Liu, Fangzhou

Hardcover

Technology & Engineering

ISBN10: 1041141203
ISBN13: 9781041141204
Publisher: CRC Press
Published: Nov 27 2025
Pages: 114
Weight: 0.80
Height: 0.38 Width: 6.14 Depth: 9.21
Language: English

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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