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Continuous-time Laguerre-based subspace identification utilising nuclear norm minimisation

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  • Miao Yu
  • Ge Guo
  • Jianchang Liu

Abstract

This paper presents a continuous-time Laguerre-based subspace identification method utilising nuclear norm minimisation. The input–output matrix equation of the systems is deduced by a bank of Laguerre filters in all-pass domain, which can deal with the un-equidistant data. Nuclear norm minimisation is adopted, instead of the truncation of dominant singular values, to obtain low-rank matrix approximations which can easily obtain the system order. Furthermore, the optimisation problem is solved by the alternating direction method of multipliers. Simulation results are provided to show the effectiveness of the proposed method.

Suggested Citation

  • Miao Yu & Ge Guo & Jianchang Liu, 2021. "Continuous-time Laguerre-based subspace identification utilising nuclear norm minimisation," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(1), pages 157-172, January.
    • Handle: RePEc:taf:tsysxx:v:52:y:2021:i:1:p:157-172
    DOI: 10.1080/00207721.2020.1823047
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