Predictive Tracking Under Persistent Disturbances and Data Errors Using [Formula Omitted] FIR Approach

Industrial processes may incur a significant loss in information under unspecified impacts and data errors. Therefore, robust predictors are required to ensure the performance. In this article, we design an [Formula Omitted] optimal finite impulse response ([Formula Omitted]-OFIR) predictor under pe...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2022-01, Vol.69 (6), p.6121
Main Authors: Shmaliy, Yuriy, Xu, Yuan, Andrade-Lucio, Jose, Ibarra-Manzano, Oscar
Format: Article
Language:English
Subjects:
Algorithms
Disturbances
Errors
Impulse response
Linear matrix inequalities
Mathematical analysis
Noise prediction
Norms
Robustness
Tracking
Ultrawideband
Online Access:Get full text
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Summary:Industrial processes may incur a significant loss in information under unspecified impacts and data errors. Therefore, robust predictors are required to ensure the performance. In this article, we design an [Formula Omitted] optimal finite impulse response ([Formula Omitted]-OFIR) predictor under persistent disturbances, measurement errors, and initial errors. The [Formula Omitted]-OFIR predictor is derived by minimizing the squared weighted Frobenius norms for each error. A suboptimal [Formula Omitted] finite impulse response (FIR) prediction algorithm is obtained using a linear matrix inequality. The [Formula Omitted]-OFIR predictive tracker is tested by simulations assuming Markov disturbances and data errors driven by the Gaussian, uniform, and industrial Cauchy heavy-tailed noise. It is shown experimentally that in predictive tracking of a moving robot using the ultrawideband technology, the [Formula Omitted]-OFIR predictor operating with full error matrices is more robust than the Kalman and unbiased FIR predictors.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2021.3087403