Safe and active parameter exploration for event-triggered control

Kazumune Hashimoto

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arXiv · 2010.09174

Safe and active parameter exploration for event-triggered control

Kazumune Hashimoto

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Abstract

This paper presents a framework of learning parameter space for event-triggered control. In particular, our goal is to find a set of parameters for the event-triggered condition, such that certain specifications on safety and convergence properties are satisfied. The exploration strategy is based on the Gaussian process-based active learning, in which, for each iteration, the parameter with having the largest variance is evaluated. Moreover, we provide a theoretical analysis, so that the derived parameter space satisfies both convergence and safety. Finally, a numerical simulation is given to illustrate the effectiveness of the approach.

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Kazumune Hashimoto. 2020-10-19. Safe and active parameter exploration for event-triggered control. https://arxiv.org/abs/2010.09174

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