Traffic Models of Periodic Event-Triggered Control Systems

TL;DR

This paper develops timing models for periodic event-triggered control systems to accurately capture their traffic patterns, using a two-step approach involving state space partitioning and LMI-based analysis.

Contribution

It introduces a novel method to construct traffic models for PETC systems by combining state space partitioning with LMI-based event-triggering analysis.

Findings

Effective traffic models for PETC systems are constructed.

The approach accurately captures system dynamics and traffic behavior.

Models facilitate improved design and analysis of control systems.

Abstract

Periodic event-triggered control (PETC) is a version of event-triggered control (ETC) that only requires to measure the plant output periodically instead of continuously. In this work, we present a construction of timing models for these PETC implementations to capture the dynamics of the traffic they generate. In the construction, we employ a two-step approach. We first partition the state space into a finite number of regions. Then in each region, the event-triggering behavior is analyzed with the help of LMIs. The state transitions among different regions result from computing the reachable state set starting from each region within the computed event time intervals.