文章基本信息

标题：Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization
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作者：Mingzhou Yin ; Andrea Iannelli ; Mohammad Khosravi 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：1237-1242
DOI：10.1016/j.ifacol.2020.12.1341
语种：English
出版社：Elsevier
摘要：AbstractThis paper proposes a new methodology in linear time-periodic (LTP) system identification. In contrast to previous methods that totally separate dynamics at different tag times for identification, the method focuses on imposing appropriate structural constraints on the linear time-invariant (LTI) reformulation of LTP systems. This method adopts a periodically-switched truncated infinite impulse response model for LTP systems, where the structural constraints are interpreted as the requirement to place the poles of the non-truncated models at the same locations for all sub-models. This constraint is imposed by combining the atomic norm regularization framework for LTI systems with the group lasso technique in regression. As a result, the estimated system is both uniform and low-order, which is hard to achieve with other existing estimators. Monte Carlo simulation shows that the grouped atomic norm method does not only show better results compared to other regularized methods, but also outperforms the subspace identification method under high noise levels in terms of model fitting.
关键词：KeywordsSystem identificationregularizationperiodic systems