PUMA
Istituto di Studi sui Sistemi Intelligenti per l'Automazione     
Alessandri A., Baglietto M., Battistelli G. Robust receding-horizon state estimation for uncertain discrete-time linear systems. In: Systems & Control Letters, vol. 54 (7) pp. 627 - 643. Elsevier, 2005.
 
 
Abstract
(English)
An approach to robust receding-horizon state estimation for discrete-time linear systems is presented. Estimates of the state variables can be obtained by minimizing a worst-case quadratic cost function according to a sliding-window strategy. 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. The stability properties of the estimation error for both the optimal filter and the approximate one have been studied and conditions to select the design parameters are proposed. Simulation results are reported to show the effectiveness of the proposed approach.
DOI: 10.1016/j.sysconle.2004.11.003
Subject State estimation
Receding horizon
Uncertain linear systems
Robustness
Minimax optimization


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