Simultaneous input and state estimation of linear discrete-time stochastic systems with input aggregate information

Sze Yong, Minghui Zhu, Emilio Frazzoli

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

2 Scopus citations

Abstract

In this paper, we present filtering algorithms for simultaneous input and state estimation of linear discrete-time stochastic systems when the unknown inputs are partially known, i.e., when some aggregate information of the unknown inputs is available as linear equality or inequality constraints. The stability and optimality properties of the filters are presented and proven using two complementary perspectives. Specifically, we confirm the intuition that the partial input information improves the performance of the filters when a linear input equality constraint is given. On the other hand, given a linear inequality constraint, we show that the estimate error covariance is decreased but the estimates may be biased.

Original languageEnglish (US)
Title of host publication54rd IEEE Conference on Decision and Control,CDC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages461-467
Number of pages7
ISBN (Electronic)9781479978861
DOIs
StatePublished - Feb 8 2015
Externally publishedYes
Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
Duration: Dec 15 2015Dec 18 2015

Other

Other54th IEEE Conference on Decision and Control, CDC 2015
CountryJapan
CityOsaka
Period12/15/1512/18/15

ASJC Scopus subject areas

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

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