NDLI: Advances in moving horizon estimation for nonlinear systems

Content Provider	IEEE Xplore Digital Library
Author	Alessandri, A. Baglietto, M. Battistelli, G. Zavala, V.
Copyright Year	2010
Description	Author affiliation: Department of Production Engineering, Thermoenergetics, and Mathematical Models, DIPTEM-University of Genoa, P.le Kennedy Pad. D, 16129 Genova, Italy (Alessandri, A.) \|\| Department of Communications, Computer and System Sciences, DIST-University of Genoa, Via Opera Pia 13, 16145, Genova, Italy (Baglietto, M.) \|\| Dipartimento di Sistemi e Informatica, Università degli Studi di Firenze, Via di S. Marta 3, 50139, Italy (Battistelli, G.) \|\| Mathematics and Computer Science Division, Argonne National Laboratory 9700 South Cass Ave, Bldg. 221, IL 60439, USA (Zavala, V.)
Abstract	In the last decade, moving horizon estimation (MHE) has emerged as a powerful technique for tackling the problem of estimating the state of a dynamic system in the presence of nonlinearities and disturbances. MHE is based on the idea of minimizing an estimation cost function defined on a sliding window composed of a finite number of time stages. The cost function is usually made up of two contributions: a prediction error computed on a recent batch of inputs and outputs; an arrival cost that serves the purpose of summarizing the past data. However, the diffusion of such techniques has been hampered by: i) the difficulty in choosing the arrival cost so as to ensure stability of the overall estimation scheme; ii) the request of an adequate computational effort on line. In this paper, both problems are addressed and possible solutions are proposed. First, by means of a novel stability analysis, it is constructively shown that under very general observability conditions a quadratic arrival cost is sufficient to ensure the stability of the estimation error provided that the weight matrix is adequately chosen. Second, a novel approximate MHE algorithm is proposed that is based on nonlinear programming sensitivity calculations. The approximate MHE algorithm has the same stability properties of the optimal one which make the overall approach suitable to be applied in real settings. Preliminary simulation results confirm the effectiveness of proposed method.
Starting Page	5681
Ending Page	5688
File Size	227166
Page Count	8
File Format	PDF
ISBN	9781424477456
ISSN	07431546
e-ISBN	9781424477463
DOI	10.1109/CDC.2010.5718126
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stability analysis Upper bound Minimization Asymptotic stability Estimation error Sensitivity
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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