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

Sze Zheng Yong, Minghui Zhu, Emilio Frazzoli

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

    1 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
    Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
    Duration: Dec 15 2015Dec 18 2015

    Publication series

    NameProceedings of the IEEE Conference on Decision and Control
    Volume54rd IEEE Conference on Decision and Control,CDC 2015
    ISSN (Print)0743-1546

    Other

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

    All Science Journal Classification (ASJC) codes

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

    Fingerprint Dive into the research topics of 'Simultaneous input and state estimation of linear discrete-time stochastic systems with input aggregate information'. Together they form a unique fingerprint.

    Cite this