A novel recursive approach for online identification of continuous-time switched nonlinear systems

Yingwei Du, Fangzhou Liu, Jianbin Qiu, Martin Buss

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


This article focuses on the identification of switched nonlinear systems, which are characterized as a collection of nonlinear dynamical systems. Each nonlinear subsystem is activated by a discrete-valued variable (switching signal). Specifically, we consider the continuous-time switched nonlinear systems in the state-space form in our article. The identification of switched nonlinear systems amounts to simultaneous estimation of the switching signal and the nonlinear dynamic subsystems via all measured state-input vectors. However, the problem is challenging and generally requires a large computational complexity to be solved. In this article, we propose a novel online approach to address the identification problem of switched nonlinear systems, which is capable to handle the measured state-input vectors in sequence. In particular, the principle used for estimating the switching signal is developed based on the subspace method. Subsequently, the integral concurrent learning identifier is extended to identify the dynamics of each subsystem recursively. The effectiveness of the proposed identification approach is demonstrated via simulation results.

Original languageEnglish
Pages (from-to)7546-7565
Number of pages20
JournalInternational Journal of Robust and Nonlinear Control
Issue number15
StatePublished - Oct 2021


  • integral concurrent learning
  • mode recognition
  • online identification
  • switched nonlinear system


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