Stability of constrained linear moving horizon estimation

Christopher V Rao, James B. Rawlings, Jay H. Lee

Research output: Contribution to journalArticlepeer-review


In this work we derive sufficient conditions for the stability of moving horizon state estimation with linear models subject to constraints on the estimate. The key result is that if the time-varying or steady-state Kalman filter covariance update is used to summarize the prior data, then the estimator is stable in the sense of an observer, even in the presence of constraints.

Original languageEnglish (US)
Pages (from-to)3387-3391
Number of pages5
JournalProceedings of the American Control Conference
StatePublished - 1999
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering


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