Self-triggered control (STC) is a sample-and-hold control method aimed at reducing communications within networked-control systems; however, existing STC mechanisms often maximize how late the next sample is, and as such they do not provide any sampling optimality in the long-term. In this work, we devise a method to construct self-triggered policies that provide near-maximal average inter-sample time (AIST) while respecting given control performance constraints. To achieve this, we rely on finite-state abstractions of a reference event-triggered control, in which early triggers are also allowed. These early triggers constitute controllable actions of the abstraction, for which an AIST-maximizing strategy can be computed by solving a mean-payoff game. We provide optimality bounds, and how to further improve them through abstraction refinement techniques.

中文翻译：

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接近最大平均采样间时间的自触发控制

自触发控制（STC）是一种采样保持控制方法，旨在减少网络控制系统内的通信。但是，现有的STC机制通常会最大化下一个样本的延迟时间，因此从长远来看，它们不提供任何样本最优性。在这项工作中，我们设计了一种方法来构造自触发策略，该策略可在遵守给定控制性能约束的同时提供接近最大的平均采样间时间（AIST）。为了实现这一点，我们依赖于参考事件触发控件的有限状态抽象，其中也允许提早触发。这些早期触发因素构成了抽象的可控动作，可以通过解决均值收益博弈来计算AIST最大化策略。我们提供最优界限，