Sampled-Data Observer Design for Linear Kuramoto-Sivashinsky Systems with Non-Local Output

TL;DR

This paper introduces a systematic method for designing sampled-data observers for Linear Kuramoto-Sivashinsky systems with non-local outputs, enhancing stability and allowing larger sampling periods through a tuning mechanism.

Contribution

It extends existing observer design methods to LK-S systems with non-local outputs using an Inter-Sample output predictor and small-gain approach for stability analysis.

Findings

Provides sufficient conditions for Input-to-Output Stability

Enables larger Maximum Allowable Sampling Periods

Demonstrates effectiveness through theoretical analysis

Abstract

The aim of this paper is to provide a novel systematic methodology for the design of sampled-data observers for Linear Kuramoto-Sivashinsky systems (LK-S) with non-local outputs. More precisely, we extend the systematic sampled-data observer design approach which is based on the use of an Inter-Sample output predictor to the class of LK-S systems. By using a small-gain methodology we provide sufficient conditions ensuring the Input-to-Output Stability (IOS) property of the estimation errors in the presence of measurement noise. Our Inter-Sample output predictor contains a tuning term which can enlarge significantly the Maximum Allowable Sampling Period (MASP).