Sampled-Data State Observation over Lossy Networks under Round-Robin Scheduling

TL;DR

This paper develops an LMI-based framework for designing dynamic observers in continuous-time systems over lossy networks with round-robin scheduling, ensuring stability despite communication losses.

Contribution

It introduces a novel LMI approach for observer gain design that adapts to scheduled communication in lossy networks, enhancing stability and robustness.

Findings

Proposed LMI framework guarantees asymptotic stability.

Numerical simulations validate the effectiveness of the method.

Dynamic gain adjustment improves observation accuracy.

Abstract

In this paper, we study the problem of continuous-time state observation over lossy communication networks. We consider the situation in which the samplers for measuring the output of the plant are spatially distributed and their communication with the observer is scheduled according to a round-robin scheduling protocol. We allow the observer gains to dynamically change in synchronization with the scheduling of communications. In this context, we propose a linear matrix inequality (LMI) framework to design the observer gains that ensure the asymptotic stability of the error dynamics in continuous time. We illustrate the effectiveness of the proposed methods by several numerical simulations.