Approximating fault detection linear interval observers using -order interval predictors
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Interval observers can be described by an autoregressive-moving-average model while ¿-order interval predictors by a moving-average model. Because an autoregressive-moving-average (ARMA) model can be approximated by a moving-average model, this allows establishing the equivalence between interval observers and interval predictors. This paper deals with the fault detection application and focuses on the equivalence between the ¿-orderintervalpredictorsand the interval observers from the point of view of the fault detection performance. The paper also proves that it is possible to obtain an equivalent ¿ - order interval predictor for a given interval observer with the same fault detection properties by the appropriate selection of the ¿ - order. A condition for selecting the minimal order that provides the ¿ - order interval predictor equivalent to a given interval observer is derived. Moreover, because the wrapping effect could be avoided by tuning properly the interval observer, we can find an equivalent ¿ - order interval predictor such that it also avoids the wrapping effect. Finally, an example based on an industrial servo actuator will be used to illustrate the derived results. Copyright © 2016 John Wiley & Sons, Ltd.
"This is the peer reviewed version of the following article:Meseguer, J., Puig, V., and Escobet, T. (2017) Approximating fault detection linear interval observers using ¿-order interval predictors. Int. J. Adapt. Control Signal Process., 31: 1040–1060., which has been published in final form at https://doi.org/10.1002/acs.2746. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."
CitationMeseguer, J., Puig, V., Escobet, T. Approximating fault detection linear interval observers using -order interval predictors. "International journal of adaptive control and signal processing", 1 Juliol 2017, vol. 31, núm. 7, p. 1040-1060.