ARX Model Estimation of Multivariable Errors-in-Variables Systems

Liu, Xin; Zhu, Yucai

doi:10.1016/j.ifacol.2018.09.111

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…The updated estimate of Σ eη can now be used to transform the lagged measurements as in Eq. (16), and new estimates of model parameters are obtained. This iterative procedure is repeated until the convergence of model parameters.…”

Section: A Model Identification For Known Order and Noise Variancesmentioning

confidence: 99%

“…They include methods such as maximum likelihood estimation [8], instrumental variable [9], [10], bias compensation [11], [12], Koopmans-Levin [13], and recursive estimation [14]. These methods have also been extended for multi-input and multi-output systems [15], [16]. A comprehensive survey on the estimation of the EIV models is described in [5], [17].…”

Section: Introductionmentioning

confidence: 99%

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Identification of Errors-in-Variables Models Using Dynamic Iterative Principal Component Analysis

Maurya

Tangirala

Narasimhan

2018

Ind. Eng. Chem. Res.

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Identification of autoregressive models with exogenous input (ARX) is a classical problem in system identification. This article considers the errors-in-variables (EIV) ARX model identification problem, where input measurements are also corrupted with noise. The recently proposed DIPCA technique solves the EIV identification problem but is only applicable to white measurement errors. We propose a novel identification algorithm based on a modified Dynamic Iterative Principal Components Analysis (DIPCA) approach for identifying the EIV-ARX model for single-input, single-output (SISO) systems where the output measurements are corrupted with coloured noise consistent with the ARX model. Most of the existing methods assume important parameters like input-output orders, delay, or noise-variances to be known. This work's novelty lies in the joint estimation of error variances, process order, delay, and model parameters. The central idea used to obtain all these parameters in a theoretically rigorous manner is based on transforming the lagged measurements using the appropriate error covariance matrix, which is obtained using estimated error variances and model parameters. Simulation studies on two systems are presented to demonstrate the efficacy of the proposed algorithm.

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Section: A Model Identification For Known Order and Noise Variancesmentioning

confidence: 99%