A parametric programming approach to moving-horizon state estimation

We propose a solution to moving-horizon state estimation that incorporates inequality constraints in both a systematic and computationally efficient way, akin to Kalman filtering. The proposed method allows the on-line constrained optimization problem involved in moving-horizon state estimation to b...

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Bibliographic Details
Published in:Automatica (Oxford) 2007-05, Vol.43 (5), p.885-891
Main Authors: Darby, Mark L., Nikolaou, Michael
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Constraints
Control theory. Systems
Exact sciences and technology
Kalman filter
Modelling and identification
Moving horizon
Parametric programming
State estimation
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Summary:We propose a solution to moving-horizon state estimation that incorporates inequality constraints in both a systematic and computationally efficient way, akin to Kalman filtering. The proposed method allows the on-line constrained optimization problem involved in moving-horizon state estimation to be solved offline, requiring only a look-up table and simple function evaluations for real-time implementation. The method is illustrated via simulations on a system that has been studied in literature.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2006.11.021