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
Alessandri, A.;Baglietto, M.;Battistelli, G.
2003-01-01
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.
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