Multipass Optimal FIR Filtering for Processes With Unknown Initial States and Temporary Mismatches

In this article, the multipass optimal finite impulse response (OFIR) filtering approach is developed for industrial processes with unknown initial conditions under temporary model mismatches. The forward and backward OFIR filters are derived in batch and fast iterative forms using recursions. The d...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2021-08, Vol.17 (8), p.5360-5368
Main Authors: Zhao, Shunyi, Shmaliy, Yuriy S., Andrade-Lucio, Jose A., Liu, Fei
Format: Article
Language:English
Subjects:
Covariance matrices
Filtration
Finite impulse response filters
Impulse response
Industrial environments
Informatics
Initial conditions
Iterative algorithms
Iterative methods
Kalman filter (KF)
Kalman filters
Matrices
optimal finite impulse response (OFIR) filter
Robustness
state estimation
temporary uncertainty
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Summary:In this article, the multipass optimal finite impulse response (OFIR) filtering approach is developed for industrial processes with unknown initial conditions under temporary model mismatches. The forward and backward OFIR filters are derived in batch and fast iterative forms using recursions. The double-pass OFIR (DOFIR) filter supported by the unbiased FIR (UFIR) filter and triple-pass OFIR (TOFIR) filter starting with some initial values are designed and extensively investigated using simulations and experimental data. It is shown that the DOFIR and TOFIR filters are able to essentially improve the performance close to the initial values and are more robust against temporary model mismatches than the Kalman, OFIR, and UFIR filters.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.3026999