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

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

doi:10.1109/TAC.2002.808470

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 journal › Article › peer-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 language	English (US)
Pages (from-to)	246-258
Number of pages	13
Journal	IEEE Transactions on Automatic Control
Volume	48
Issue number	2
DOIs	https://doi.org/10.1109/TAC.2002.808470
State	Published - Feb 2003
Externally published	Yes

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

Online availability

10.1109/TAC.2002.808470

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Cite this

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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.",

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note = "Manuscript received September 7, 2001; revised May 29, 2002 and August 28, 2002. Recommended by Associate Editor T. Parisini. This work was supported by the Industrial Members of the Texas-Wisconsin Modeling and Control Consortium, and by the National Science Foundation under Grant CTS-9708497.",

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AU - Rao, Christopher V.

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AU - Mayne, David Q.

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N2 - 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.

AB - 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.

KW - Constraints

KW - Model predictive control (MPC)

KW - Moving horizon estimation (MHE)

KW - Optimization

KW - State estimation

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

Abstract

Keywords

ASJC Scopus subject areas

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