ARX modeling of unstable linear systems

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• 作者：Miguel Galrinho ^{galrinho@kth.se} ; Niklas Everitt ^{neveritt@kth.se} ; Hå ; kan Hjalmarsson ^{hjalmars@kth.se}
• 关键词：System identification ; Polynomial models ; Closed-loop identification
• 刊名：Automatica
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：75
• 期：Complete
• 页码：167-171
• 全文大小：502 K
• 卷排序：75

文摘

High-order ARX models can be used to approximate a quite general class of linear systems in a parametric model structure, and well-established methods can then be used to retrieve the true plant and noise models from the ARX polynomials. However, this commonly used approach is only valid when the plant is stable or if the unstable poles are shared with the true noise model. In this contribution, we generalize this approach to allow the unstable poles not to be shared, by introducing modifications to correctly retrieve the noise model and noise variance.