Computing the average inter-sample time of event-triggered control using quantitative automata

TL;DR

This paper introduces a formal method using finite-state abstractions to quantify the average inter-sample time in event-triggered control systems, enabling precise analysis of resource savings in cyber-physical systems.

Contribution

It presents a novel approach to compute the smallest average inter-sample time for ETC systems using $l$-complete abstractions and cycle analysis, applicable to nonlinear systems.

Findings

Successfully quantifies traffic in linear ETC systems.

Robust to small model uncertainties.

Applicable to nonlinear systems for SAIST computation.

Abstract

Event-triggered control (ETC) is a major recent development in cyber-physical systems due to its capability of reducing resource utilization in networked devices. However, while most of the ETC literature reports simulations indicating massive reductions in the sampling required for control, no method so far has been capable of quantifying these results. In this work, we propose an approach through finite-state abstractions to do formal quantification of the traffic generated by ETC of linear systems, in particular aiming at computing its smallest average inter-sample time (SAIST). The method involves abstracting the traffic model through $l$-complete abstractions, finding the cycle of minimum average length in the graph associated to it, and verifying whether this cycle is an infinitely recurring traffic pattern. The method is proven to be robust to sufficiently small model uncertainties, which allows its application to compute the SAIST of ETC of nonlinear systems.