论文标题
安全有效的参数探索，用于事件触发的控制
Safe and active parameter exploration for event-triggered control
论文作者
论文摘要
本文提出了一个学习参数空间的框架，用于事件触发的控制。特别是，我们的目标是为事件触发的条件找到一组参数，以便满足有关安全性和收敛属性的某些规格。探索策略基于基于高斯过程的主动学习，在该学习中，对于每种迭代，都评估具有最大方差的参数。此外，我们提供了理论分析，以便派生的参数空间满足收敛和安全性。最后，给出数值模拟以说明方法的有效性。
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.
