Nuclear norm minimization in subspace based continuous-time Hammerstein system identification

Mingxiang Dai, Jingxin Zhang, Li Chai

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

2 Citations (Scopus)


A novel method combining the nuclear norm minimization (NNM) and continuous-time (CT) subspace identification method (CSIM) is proposed to identify the CT Hammerstein model with little priori information. The nuclear norm minimization, which is the heuristic convex relaxation of the minimum rank constraint, is applied to the CT subspace identification method, for the purpose of improving the robustness and accuracy of identification. The proposed method can perform the identification well without the priori information about the Hammerstein model, which not only reduces the complexity of the identification problem but also broaden its applications. The nonlinear block of Hammerstein model is approximated with the pseudospectral method, which replaces the nonlinear function with Lagrange basis functions. A typical numerical example is presented to verify the NNMCSI method and the identification results are compared with the refined instrumental variable method.

Original languageEnglish
Title of host publication11th IEEE International Conference on Control and Automation, IEEE ICCA 2014
Subtitle of host publicationTaichung, Taiwan; 18-20 June 2014
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Print)9781479928378, 9781479928385
Publication statusPublished - 2014
EventIEEE International Conference on Control and Automation 2014 - Taichung, Taiwan
Duration: 18 Jun 201420 Jun 2014
Conference number: 11th


ConferenceIEEE International Conference on Control and Automation 2014
Abbreviated titleIEEE ICCA 2014

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