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Safe event-triggered learning for sampled-data systems
A. Begzadić, A. Lederer, S. Hirche, J. Cortés, S. L. Herbert
Proceedings of the American Control Conference, New Orleans, Louisiana, 2026, to appear

Abstract

Control Barrier Functions provide a principled framework for ensuring safety, yet their reliance on accurate system models is a critical limitation for practical deployment. Moreover, the majority of theory relies on continuous-time guarantees that may not hold for discrete controllers. To address these challenges, we employ Gaussian processes to learn the components of the CBF safety condition, which depend on the unknown system dynamics, directly from data. Since controllers typically need to be implemented in a sampled-data fashion, we develop robust CBF conditions that account for inter-sampling effects to ensure the safety of unknown control-affine systems. The Gaussian Process model is updated online under a bimodal control framework that switches between control-focused and learning-focused phases, and its uncertainty is leveraged to guarantee satisfaction of the CBF conditions with high probability. The effectiveness of the proposed framework is demonstrated in simulations.

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