Simultaneous mode, input and state estimation for switched linear stochastic systems

Sze Zheng Yong, Minghui Zhu, Emilio Frazzoli

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose a filtering algorithm for simultaneously estimating the mode, input and state of hidden mode switched linear stochastic systems with unknown inputs. Using a multiple-model approach with a bank of linear input and state filters for each mode, our algorithm relies on the ability to find the most probable model as a mode estimate, which we show is possible with input and state filters by identifying a key property, that a particular residual signal we call generalized innovation is a Gaussian white noise. We also provide an asymptotic analysis for the proposed algorithm and provide sufficient conditions for asymptotically achieving convergence to the true model (consistency), or to the “closest” model according to an information-theoretic measure (convergence). A simulation example of intention-aware vehicles at an intersection is given to demonstrate the effectiveness of our approach.

Original languageEnglish (US)
Pages (from-to)640-661
Number of pages22
JournalInternational Journal of Robust and Nonlinear Control
Volume31
Issue number2
DOIs
StatePublished - Jan 25 2021

Keywords

  • nonlinear filtering
  • state and input estimation
  • switched systems
  • uncertain systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Chemical Engineering(all)
  • Biomedical Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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