System Identification Based on Invariant Subspace

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Original languageEnglish
Journal / PublicationIEEE Transactions on Automatic Control
Publication statusOnline published - 30 Mar 2021


This paper proposes a novel system identification method based on the notion of invariant subspace. It is shown that when the system input and output asymptotically converge onto an invariant subspace, a new form of regression can be obtained. New identification algorithms are then developed based on the obtained regression. The proposed method has several distinctive advantages originating from both time-domain and frequency-domain approaches. They include (i) linear continuous-time models can be identified from slowly sampled input/output data; (ii) consistency of the model parameters can be established in an error-in-variables framework; (iii) the global optimum can be found by solving two linear least-square problems; and (iv) the identification algorithms can be implemented online with explicit convergence rates. The theoretic results are tested by numerical examples to show the effectiveness of the proposed method.

Research Area(s)

  • Computational methods, Convergence, Data models, Dynamical systems, Linear systems, Mathematical model, Sampled data, Standards, System identification, Time-domain analysis, Time-frequency analysis