Abstract
This paper presents an overview of moving horizon state estimation for discrete time linear and nonlinear systems. The equivalence between optimal linear filtering and the batch least squares problem for linear plants is reviewed. A moving horizon linear estimator is introduced and shown also to be equivalent to the optimal filter. Constraints are added to the moving horizon estimator to improve robustness to modeling errors. Finally, nonlinear batch and moving horizon estimators are discussed and shown to be nominally stable. Previous implementations of these techniques in the process control literature are reviewed.
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© 1995 Springer Science+Business Media Dordrecht
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Muske, K.R., Rawlings, J.B. (1995). Nonlinear Moving Horizon State Estimation. In: Berber, R. (eds) Methods of Model Based Process Control. NATO ASI Series, vol 293. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0135-6_14
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DOI: https://doi.org/10.1007/978-94-011-0135-6_14
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-4061-7
Online ISBN: 978-94-011-0135-6
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