Moving horizon estimation with decimated observations

• Type: Article in International Conference Proceedings Book
• Date: 2010

Authors

Barreiro, RF (Author) Other; Pedro Aguiar, A (Author) Other; Lemos, JM (Author) Other

Conference proceedings International

Title: IFAC Proceedings Volumes (IFAC-PapersOnline)

Pages: 296-301

8th IFAC Symposium on Nonlinear Control Systems, NOLCOS 2010

Bologna, 1 September 2010 through 3 September 2010

Indexing

ISI Web of Knowledge

Scopus - 3 Citations

Other information

Authenticus ID: P-007-ZQW

DOI: 10.3182/20100901-3-it-2016.00267

Abstract (EN): This paper addresses the problem of moving horizon (MH) state estimation of discrete lumped nonlinear systems. It is assumed that the measurements of the observed variables are not available at every sampling instant (decimated observations). An estimation algorithm is provided for that purpose, together with results on its convergence. It is shown that, under convenient assumptions, the estimation error is bounded, with a bound that grows with the number of samples between consecutive observations. The algorithm features are illustrated by simulations concerning the application to state estimation in a model of the HIV-1 infection. The simulations show that the MH estimator exhibits superior performance over the extended Kalman filter. This difference of performance increases with the growth of the time interval between consecutive measurements. © 2010 IFAC.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6