Mode set focused hybrid estimation

Theresa Rienmüller; Michael Hofbaur; Louise Travé-Massuyès; Mehdi Bayoudh

International Journal of Applied Mathematics and Computer Science (2013)

  • Volume: 23, Issue: 1, page 131-144
  • ISSN: 1641-876X

Abstract

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Estimating the state of a hybrid system means accounting for the mode of operation or failure and the current state of the continuously valued entities concurrently. Existing hybrid estimation schemes try to overcome the problem of an exponentially growing number of possible mode-sequence/continuous-state combinations by merging hypotheses and/or deducing likelihood measures to identify tractable sets of the most likely hypotheses. However, they still suffer from unnecessarily high computational costs as the number of possible modes increases. Hybrid diagnosis schemes, on the other hand, estimate the current mode of operation/failure only, thus leaving the continuous evolution of the system implicit. This paper proposes a novel scheme that uses a combination of both the approaches in order to define posterior transition probabilities between the specified modes of the hybrid system, hence focusing better on relevant hypotheses. In order to demonstrate the effectiveness of the proposed method, the algorithm is applied to a satellite attitude control system and compared with existing hybrid estimation/diagnosis schemes, such as the Interacting Multiple Model (IMM) algorithm, a purely parity based method (HyDiag), and an existing hybrid Mode Estimation (hME) algorithm.

How to cite

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Theresa Rienmüller, et al. "Mode set focused hybrid estimation." International Journal of Applied Mathematics and Computer Science 23.1 (2013): 131-144. <http://eudml.org/doc/251345>.

@article{TheresaRienmüller2013,
abstract = {Estimating the state of a hybrid system means accounting for the mode of operation or failure and the current state of the continuously valued entities concurrently. Existing hybrid estimation schemes try to overcome the problem of an exponentially growing number of possible mode-sequence/continuous-state combinations by merging hypotheses and/or deducing likelihood measures to identify tractable sets of the most likely hypotheses. However, they still suffer from unnecessarily high computational costs as the number of possible modes increases. Hybrid diagnosis schemes, on the other hand, estimate the current mode of operation/failure only, thus leaving the continuous evolution of the system implicit. This paper proposes a novel scheme that uses a combination of both the approaches in order to define posterior transition probabilities between the specified modes of the hybrid system, hence focusing better on relevant hypotheses. In order to demonstrate the effectiveness of the proposed method, the algorithm is applied to a satellite attitude control system and compared with existing hybrid estimation/diagnosis schemes, such as the Interacting Multiple Model (IMM) algorithm, a purely parity based method (HyDiag), and an existing hybrid Mode Estimation (hME) algorithm.},
author = {Theresa Rienmüller, Michael Hofbaur, Louise Travé-Massuyès, Mehdi Bayoudh},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {hybrid systems; fault detection and diagnosis; monitoring and estimation},
language = {eng},
number = {1},
pages = {131-144},
title = {Mode set focused hybrid estimation},
url = {http://eudml.org/doc/251345},
volume = {23},
year = {2013},
}

TY - JOUR
AU - Theresa Rienmüller
AU - Michael Hofbaur
AU - Louise Travé-Massuyès
AU - Mehdi Bayoudh
TI - Mode set focused hybrid estimation
JO - International Journal of Applied Mathematics and Computer Science
PY - 2013
VL - 23
IS - 1
SP - 131
EP - 144
AB - Estimating the state of a hybrid system means accounting for the mode of operation or failure and the current state of the continuously valued entities concurrently. Existing hybrid estimation schemes try to overcome the problem of an exponentially growing number of possible mode-sequence/continuous-state combinations by merging hypotheses and/or deducing likelihood measures to identify tractable sets of the most likely hypotheses. However, they still suffer from unnecessarily high computational costs as the number of possible modes increases. Hybrid diagnosis schemes, on the other hand, estimate the current mode of operation/failure only, thus leaving the continuous evolution of the system implicit. This paper proposes a novel scheme that uses a combination of both the approaches in order to define posterior transition probabilities between the specified modes of the hybrid system, hence focusing better on relevant hypotheses. In order to demonstrate the effectiveness of the proposed method, the algorithm is applied to a satellite attitude control system and compared with existing hybrid estimation/diagnosis schemes, such as the Interacting Multiple Model (IMM) algorithm, a purely parity based method (HyDiag), and an existing hybrid Mode Estimation (hME) algorithm.
LA - eng
KW - hybrid systems; fault detection and diagnosis; monitoring and estimation
UR - http://eudml.org/doc/251345
ER -

References

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