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 least squares cost function according to a sliding-window strategy. The resulting optimal robust filter can be approximated by a simpler and computationally efficient estimator. Stability properties are proved for both proposed filters. Specifically, the estimation errors of such filters converge exponentially to zero when the system is not affected by noise, and a bounding sequence can be given in the presence of bounded system and measurement disturbances. Simulation results are reported to show the effectiveness of the proposed approach.
A minimax receding-horizon estimator for uncertain discrete-time linear systems / A. Alessandri; M. Baglietto; G. Battistelli. - STAMPA. - (2004), pp. 205-210. (Intervento presentato al convegno American Control Conference, 2004 tenutosi a Boston, USA).
A minimax receding-horizon estimator for uncertain discrete-time linear systems
BATTISTELLI, GIORGIO
2004
Abstract
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 least squares cost function according to a sliding-window strategy. The resulting optimal robust filter can be approximated by a simpler and computationally efficient estimator. Stability properties are proved for both proposed filters. Specifically, the estimation errors of such filters converge exponentially to zero when the system is not affected by noise, and a bounding sequence can be given in the presence of bounded system and measurement disturbances. Simulation results are reported to show the effectiveness of the proposed approach.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.