AUTHORS: Hong Son Hoang, Remy Baraille
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ABSTRACT: This paper presents an optimal filtering approach to state and model error (ME) estimation problem, with a deterministic or stochastic ME. The approach is based on the adaptive filtering (AF) algorithm which is aimed at overcoming the difficulties in the filter design with very high dimensionality of the dynamic systems. The objective is to design a filtering algorithm offering potential for improvement of numerical accuracy and reduction of computational burden. A hypothesis on the structure of ME is introduced. The improvement of the AF performance is achieved by tuning some pertinent parameters of the filter gain as well as bias parameters to minimize the prediction error of the system output. Numerical experiments are presented to illustrate the performance of the proposed approach.
KEYWORDS: Dynamic system, Model error, Adaptive filter, Minimal mean prediction error, Filter stability
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