Feedback for nonlinear system identification

Thiago Burghi, Maarten Schoukens, Rodolphe Sepulchre

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
3 Downloads (Pure)


Motivated by neuronal models from neuroscience, we consider the system identification of simple feedback structures whose behaviors include nonlinear phenomena such as excitability, limit-cycles and chaos. We show that output feedback is sufficient to solve the identification problem in a two-step procedure. First, the nonlinear static characteristic of the system is extracted, and second, using a feedback linearizing law, a mildly nonlinear system with an approximately-finite memory is identified. In an ideal setting, the second step boils down to the identification of a LTI system. To illustrate the method in a realistic setting, we present numerical simulations of the identification of two classical systems that fit the assumed model structure.

Original languageEnglish
Title of host publication2019 18th European Control Conference, ECC 2019
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)978-3-907144-00-8
Publication statusPublished - 1 Jun 2019
Event18th European Control Conference, ECC 2019 - Naples, Italy, Naples, Italy
Duration: 25 Jun 201928 Jun 2019
Conference number: 18


Conference18th European Control Conference, ECC 2019
Abbreviated titleECC 2019
Other18th European Control Conference (ECC 2019) (in cooperation with IFAC)
Internet address


  • Approximately-finite memory
  • Excitability
  • Nonlinear systems
  • Output feedback
  • Systems identification


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