Scalable Traffic Models for Scheduling of Linear Periodic Event-Triggered Controllers
Abstract
This paper addresses the problem of modeling and scheduling the transmissions generated by multiple event-triggered control (ETC) loops sharing a network. We present a method to build a symbolic traffic model of periodic ETC (PETC), which by construction provides an exact simulation of such traffic. The model is made in such a way as to avoid the combinatorial explosion that is typical of symbolic models in many applications. It is augmented with early triggering actions that can be used by a scheduler to mitigate communication conflicts. The complete networked control system is then modeled as a network of timed game automata, for which existing tools can generate a strategy that avoids communication conflicts, while keeping early triggers to a minimum. By construction, our proposed symbolic model is a quotient model of the PETC. It is relatively fast to build, and it generates few to no spurious transitions. We finally demonstrate modeling and scheduling for a numerical example.
Cite
@article{arxiv.2003.07642,
title = {Scalable Traffic Models for Scheduling of Linear Periodic Event-Triggered Controllers},
author = {Gabriel de Albuquerque Gleizer and Manuel Mazo},
journal= {arXiv preprint arXiv:2003.07642},
year = {2021}
}
Comments
Accepted at the 21st IFAC World Congress (IFAC 2020). Reproducibility note: v4 fixes an issue in the (to-be) published version, where wrong values were reported for the R matrices used in the example LQR controllers. Qualitatively, results are unchanged, but values have been fixed for reproducibility