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Constrained state estimation for stochastic jump systems: moving horizon approach

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  • Qing Sun
  • Cheng-Chew Lim
  • Fei Liu

Abstract

We discuss the state estimation advantages for a class of linear discrete-time stochastic jump systems, in which a Markov process governs the operation mode, and the state variables and disturbances are subject to inequality constraints. The horizon estimation approach addressed the constrained state estimation problem, and the Bayesian network technique solved the stochastic jump problem. The moving horizon state estimator designed in this paper can produce the constrained state estimates with a lower error covariance than under the unconstrained counterpart. This new estimation method is used in the design of the restricted state estimator for two practical applications.

Suggested Citation

  • Qing Sun & Cheng-Chew Lim & Fei Liu, 2017. "Constrained state estimation for stochastic jump systems: moving horizon approach," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(5), pages 1009-1021, April.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:5:p:1009-1021
    DOI: 10.1080/00207721.2016.1229080
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    References listed on IDEAS

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    1. Dorit Hochbaum, 2007. "Complexity and algorithms for nonlinear optimization problems," Annals of Operations Research, Springer, vol. 153(1), pages 257-296, September.
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