Cubature H∞ information filter

Bharani Chandra; Da-Wei Gu; Ian Postlethwaite

Cubature H∞ information filter

Bharani Chandra, Da-Wei Gu, Ian Postlethwaite

Research output: Contribution to conference › Paper › peer-review

2 Citations (Scopus)

Abstract

This paper presents a state estimation algorithm referred to as a cubature H∞ information filter (CH∞IF) for nonlinear systems. The proposed algorithm is developed from a cubature Kalman filter, an H∞ filter and an extended information filter. The CH ∞IF is a derivative free filter, where the information state vector and information matrix are propagated rather than the state vector and error covariance matrix. Furthermore, the CH∞IF is extended for multi-sensor state estimation. The efficacy of the CH∞IF is demonstrated by a simulation example of a permanent magnet synchronous motor in the presence of Gaussian and non-Gaussian noises.

Original language	English
Publication status	Published - Jul 2013
Event	ECC13 - European Control Conference - Zurich, Switzerland Duration: 1 Jul 2013 → …

Conference

Conference	ECC13 - European Control Conference
Period	1/07/13 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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https://controls.papercept.net/conferences/conferences/ECC13/program/ECC13_ContentListWeb_3.html#thc4_04

Cite this

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title = "Cubature H∞ information filter",

abstract = "This paper presents a state estimation algorithm referred to as a cubature H∞ information filter (CH∞IF) for nonlinear systems. The proposed algorithm is developed from a cubature Kalman filter, an H∞ filter and an extended information filter. The CH ∞IF is a derivative free filter, where the information state vector and information matrix are propagated rather than the state vector and error covariance matrix. Furthermore, the CH∞IF is extended for multi-sensor state estimation. The efficacy of the CH∞IF is demonstrated by a simulation example of a permanent magnet synchronous motor in the presence of Gaussian and non-Gaussian noises.",

author = "Bharani Chandra and Da-Wei Gu and Ian Postlethwaite",

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T1 - Cubature H∞ information filter

AU - Chandra, Bharani

AU - Gu, Da-Wei

AU - Postlethwaite, Ian

PY - 2013/7

Y1 - 2013/7

N2 - This paper presents a state estimation algorithm referred to as a cubature H∞ information filter (CH∞IF) for nonlinear systems. The proposed algorithm is developed from a cubature Kalman filter, an H∞ filter and an extended information filter. The CH ∞IF is a derivative free filter, where the information state vector and information matrix are propagated rather than the state vector and error covariance matrix. Furthermore, the CH∞IF is extended for multi-sensor state estimation. The efficacy of the CH∞IF is demonstrated by a simulation example of a permanent magnet synchronous motor in the presence of Gaussian and non-Gaussian noises.

AB - This paper presents a state estimation algorithm referred to as a cubature H∞ information filter (CH∞IF) for nonlinear systems. The proposed algorithm is developed from a cubature Kalman filter, an H∞ filter and an extended information filter. The CH ∞IF is a derivative free filter, where the information state vector and information matrix are propagated rather than the state vector and error covariance matrix. Furthermore, the CH∞IF is extended for multi-sensor state estimation. The efficacy of the CH∞IF is demonstrated by a simulation example of a permanent magnet synchronous motor in the presence of Gaussian and non-Gaussian noises.

UR - https://www.scopus.com/pages/publications/84893324140

M3 - Paper

T2 - ECC13 - European Control Conference

Y2 - 1 July 2013

ER -

Cubature H∞ information filter

Abstract

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UN SDGs

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