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arXiv · 2403.08054

Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions

Tzu-Yuan Huang·Sihua Zhang·Xiaobing Dai·Alexandre Capone·Velimir Todorovski·Stefan Sosnowski·Sandra Hirche
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Abstract

In many control system applications, state constraint satisfaction needs to be guaranteed within a prescribed time. While this issue has been partially addressed for systems with known dynamics, it remains largely unaddressed for systems with unknown dynamics. In this paper, we propose a Gaussian process-based time-varying control method that leverages backstepping and control barrier functions to achieve safety requirements within prescribed time windows for control affine systems. It can be used to keep a system within a safe region or to make it return to a safe region within a limited time window. These properties are cemented by rigorous theoretical results. The effectiveness of the proposed controller is demonstrated in a simulation of a robotic manipulator.

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Tzu-Yuan Huang, Sihua Zhang, Xiaobing Dai, Alexandre Capone, Velimir Todorovski, Stefan Sosnowski, Sandra Hirche. 2024-03-12. Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions. https://arxiv.org/abs/2403.08054

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