System-Level Recurrent State Estimators for Affine Systems Subject to Data Losses Modeled by Automata

Syed M. Hassaan, Sze Zheng Yong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper proposes a robust output feedback state estimator for uncertain/bounded-error affine systems subject to data losses modeled by an automaton. Specifically, by introducing a novel property known as recurrent recovery, where the estimation errors are required to be recurrent to some minimum recovery levels at each node of the data loss automata, we design a robust estimator design that guarantees that the state estimation errors remain bounded in a recurrent manner despite worst-case realizations of process and sensor noise/uncertainties in addition to missing data. Our design can directly deal with infinite-horizon missing data specifications modeled by automata by recasting the problem into multiple finite-horizon problems of varying lengths, which results in an optimization-based approach with only a finite number of constraints. Moreover, our design is built upon system-level parameterization and for this purpose, we propose a novel affine output feedback strategy that also contributes to the literature of finite-horizon optimal control.

Original languageEnglish (US)
Title of host publication2022 IEEE 61st Conference on Decision and Control, CDC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4118-4124
Number of pages7
ISBN (Electronic)9781665467612
DOIs
StatePublished - 2022
Externally publishedYes
Event61st IEEE Conference on Decision and Control, CDC 2022 - Cancun, Mexico
Duration: Dec 6 2022Dec 9 2022

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2022-December
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference61st IEEE Conference on Decision and Control, CDC 2022
Country/TerritoryMexico
CityCancun
Period12/6/2212/9/22

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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