The problem of estimating the state of discrete-time linear systems when uncertainties affect the system matrices is addressed. A quadratic cost function is considered, involving a finite number of recent measurements and a prediction vector. This leads to state the estimation problem in the form of a regularized least-squares one with uncertain data. The optimal solution (involving on-line scalar minimization) together with a suitable closed-form approximation are given. For both the resulting receding-horizon estimators convergence results are derived and an operating procedure to select the design parameters is proposed.

"Robust receding-horizon estimation for uncertain discrete-time linear systems / A. Alessandri; M. Baglietto; G. Battistelli. - STAMPA. - (2003), pp. 1459-1464. (Intervento presentato al convegno European Control Conference 2003 tenutosi a Cambridge, United Kingdom).

"Robust receding-horizon estimation for uncertain discrete-time linear systems

BATTISTELLI, GIORGIO
2003

Abstract

The problem of estimating the state of discrete-time linear systems when uncertainties affect the system matrices is addressed. A quadratic cost function is considered, involving a finite number of recent measurements and a prediction vector. This leads to state the estimation problem in the form of a regularized least-squares one with uncertain data. The optimal solution (involving on-line scalar minimization) together with a suitable closed-form approximation are given. For both the resulting receding-horizon estimators convergence results are derived and an operating procedure to select the design parameters is proposed.
2003
Proceedings of the 2003 European Control Conference
European Control Conference 2003
Cambridge, United Kingdom
A. Alessandri; M. Baglietto; G. Battistelli
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/779217
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