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Approximate Robust Control of Uncertain Dynamical Systems

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arxiv 1903.00220 v1 pith:ES7EVN2Z submitted 2019-03-01 cs.SY cs.ROcs.SY

classification cs.SYcs.RO
keywords controlrobustsystemsnon-linearproblemuncertainaimsapplied
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This work studies the design of safe control policies for large-scale non-linear systems operating in uncertain environments. In such a case, the robust control framework is a principled approach to safety that aims to maximize the worst-case performance of a system. However, the resulting optimization problem is generally intractable for non-linear systems with continuous states. To overcome this issue, we introduce two tractable methods that are based either on sampling or on a conservative approximation of the robust objective. The proposed approaches are applied to the problem of autonomous driving.

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Cited by 1 Pith paper

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  1. DIAL: Distribution-Informed Adaptive Learning of Multi-Task Constraints for Safety-Critical Systems

    cs.LG 2025-01 conditional novelty 6.0 of 10

    DIAL learns a Beta-distributed safety constraint model from multi-task demonstrations and adapts it to new tasks via a tuned CVaR risk level, improving safety in RL transfer benchmarks.

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