Constrained state estimation for nonlinear discrete-time systems: Stability and moving horizon approximations

Christopher V. Rao, James B. Rawlings, David Q. Mayne

Research output: Contribution to journalArticlepeer-review

Abstract

State estimator design for a nonlinear discrete-time system is a challenging problem, further complicated when additional physical insight is available in the form of inequality constraints on the state variables and disturbances. One strategy for constrained state estimation is to employ online optimization using a moving horizon approximation. In this article we propose a general theory for constrained moving horizon estimation. Sufficient conditions for asymptotic and bounded stability are established. We apply these results to develop a practical algorithm for constrained linear and nonlinear state estimation. Examples are used to illustrate the benefits of constrained state estimation. Our framework is deterministic.

Original languageEnglish (US)
Pages (from-to)246-258
Number of pages13
JournalIEEE Transactions on Automatic Control
Volume48
Issue number2
DOIs
StatePublished - Feb 2003

Keywords

  • Constraints
  • Model predictive control (MPC)
  • Moving horizon estimation (MHE)
  • Optimization
  • State estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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