Finite-dimensional risk-sensitive filters and smoothers for discrete-time nonlinear systems

Subhrakanti Dey; John B. Moore

doi:10.1109/9.769381

Finite-dimensional risk-sensitive filters and smoothers for discrete-time nonlinear systems

Subhrakanti Dey, John B. Moore

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

Abstract

Finite-dimensional optimal risk-sensitive filters and smoothers are obtained for discrete-time nonlinear systems by adjusting the standard exponential of a quadratic risk-sensitive cost index to one involving the plant nonlinearity. It is seen that these filters and smoothers are the same as those for a fictitious linear plant with the exponential of squared estimation error as the corresponding risk-sensitive cost index. Such finite-dimensional filters do not exist for nonlinear systems in the case of minimum variance filtering and control.

Original language	English
Pages (from-to)	1234-1239
Number of pages	6
Journal	IEEE Transactions on Automatic Control
Volume	44
Issue number	6
DOIs	https://doi.org/10.1109/9.769381
Publication status	Published - 1999

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abstract = "Finite-dimensional optimal risk-sensitive filters and smoothers are obtained for discrete-time nonlinear systems by adjusting the standard exponential of a quadratic risk-sensitive cost index to one involving the plant nonlinearity. It is seen that these filters and smoothers are the same as those for a fictitious linear plant with the exponential of squared estimation error as the corresponding risk-sensitive cost index. Such finite-dimensional filters do not exist for nonlinear systems in the case of minimum variance filtering and control.",

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AB - Finite-dimensional optimal risk-sensitive filters and smoothers are obtained for discrete-time nonlinear systems by adjusting the standard exponential of a quadratic risk-sensitive cost index to one involving the plant nonlinearity. It is seen that these filters and smoothers are the same as those for a fictitious linear plant with the exponential of squared estimation error as the corresponding risk-sensitive cost index. Such finite-dimensional filters do not exist for nonlinear systems in the case of minimum variance filtering and control.

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JO - IEEE Transactions on Automatic Control

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Finite-dimensional risk-sensitive filters and smoothers for discrete-time nonlinear systems

Abstract

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