State-feedback control of Markov jump linear systems with hidden-Markov mode observation

Masaki Ogura, Ahmet Cetinkaya, Tomohisa Hayakawa, Victor M. Preciado

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

Abstract

In this paper, we study state-feedback control of Markov jump linear systems with partial information about the mode signal responsible for switching between dynamic modes. We assume that the controller can only access random samples of the mode signal according to a hidden-Markov observation process. Our formulation provides a novel framework to analyze and design feedback control laws for various Markov jump linear systems previously studied in the literature, such as the cases of (i) clustered observations, (ii) detector-based observations, and (iii) periodic observations. We present a procedure to transform the closed-loop system with hidden-Markov observations into a standard Markov jump linear system while preserving stability, H2 norm, and H norm. Furthermore, based on this transformation, we propose a set of Linear Matrix Inequalities (LMI) to design feedback control laws for stabilization, H2 suboptimal control, and H suboptimal control of discrete-time Markov jump linear systems under hidden-Markov observations of the mode signals. We conclude by illustrating our results with some numerical examples.

Original languageEnglish
Pages (from-to)65-72
Number of pages8
JournalAutomatica
Volume89
DOIs
Publication statusPublished - 2018 Mar
Externally publishedYes

Keywords

  • H control
  • H control
  • Markov chains
  • Stabilization
  • State-feedback control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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