Feedback for nonlinear system identification

Conference Object
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Schoukens, M 
Sepulchre, Rodolphe  ORCID logo

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.

Excitability, Approximately-finite memory, Systems identification, Nonlinear systems, Output feedback
Journal Title
2019 18th European Control Conference, ECC 2019
Conference Name
European Control Conference (ECC)
Journal ISSN
Volume Title
Institute of Electrical and Electronics Engineers
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European Research Council (670645)