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• W3109214580 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." @default.
• W3109214580 created "2020-12-07" @default.
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• W3109214580 date "2020-10-09" @default.
• W3109214580 modified "2023-09-25" @default.
• W3109214580 title "Safe and active parameter exploration for event-triggered control" @default.
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• W3109214580 doi "https://doi.org/10.1109/icca51439.2020.9264438" @default.
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