Self-Triggered Control for Near-Maximal Average Inter-Sample Time
G. de Albuquerque Gleizer (TU Delft - Team Manuel Mazo Jr)
K.N. Madnani (TU Delft - Team Manuel Mazo Jr)
M. Mazo (TU Delft - Team Manuel Mazo Jr)
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Abstract
Self-triggered control (STC) is a sample-and-hold control method aimed at reducing communications in networked-control systems; however, existing STC mechanisms often maximize how late the next sample is, thus not optimizing sampling performance 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, while also allowing earlier samples. These early triggers constitute controllable actions of the abstraction, for which an AIST-maximizing strategy can be obtained by solving a mean-payoff game. We provide optimality bounds, and how to further improve them through abstraction refinement techniques.