The one-step-ahead prediction problem for systems subject to parameter uncertainty in the system dynamics and noise statistics is considered, The objective is the design of a robust predictor that minimizes an upper bound of the error covariance, Sufficient and necessary conditions for the existence of such an optimal robust estimator are given. The computation of the predictor is based on the stabilizing solution of a suitable H(i)nfinity-type Riccati equation, In the uncertainty-free case the robust predictor reduces to the standard Kalman predictor.
OPTIMAL-DESIGN OF ROBUST PREDICTORS FOR LINEAR DISCRETE-TIME-SYSTEMS
DE NICOLAO, GIUSEPPE
1995-01-01
Abstract
The one-step-ahead prediction problem for systems subject to parameter uncertainty in the system dynamics and noise statistics is considered, The objective is the design of a robust predictor that minimizes an upper bound of the error covariance, Sufficient and necessary conditions for the existence of such an optimal robust estimator are given. The computation of the predictor is based on the stabilizing solution of a suitable H(i)nfinity-type Riccati equation, In the uncertainty-free case the robust predictor reduces to the standard Kalman predictor.File in questo prodotto:
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