On Improving TLS Identication Results Using Nuisance Variables with Application on PMSM
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Date
2021-11-13
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Mark
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IEEE
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Abstract
This article presents a novel total least-squares based method for errors-in-variables model identication with a known structure. This method considers the errors of both input and output variables and thus achieves more accurate estimates compared to conventional ordinary least-squares based methods. The introduced method consists of two recursive total least-squares algorithms connected in a hierarchical structure, which allows for exploitation of nuisance variables and a priori known structure of the identied model. The total least-squares (TLS) method is introduced, and a new “nuisance improved hierarchical total least-squares” (nHTLS) method is derived. Its properties are discussed and proved by simulations. Furthermore, the method is applied in a practical experiment consisting of the state-space identication of the permanent magnet synchronous motor (PMSM). The introduced method is compared with TLS and proven to provide measurably superior dynamical behavior and smaller estimation error of results.
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IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. 2021, p. 1-6.
https://ieeexplore.ieee.org/document/9589402
https://ieeexplore.ieee.org/document/9589402
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Peer-reviewed
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en
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(C) IEEE