CUED Publications database

Feedback for nonlinear system identification

Burghi, T and Schoukens, M and Sepulchre, R (2019) Feedback for nonlinear system identification. In: UNSPECIFIED pp. 1344-1349..

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© 2019 EUCA. 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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Control
Depositing User: Cron Job
Date Deposited: 08 Oct 2019 01:09
Last Modified: 28 Nov 2019 02:49
DOI: doi:10.23919/ECC.2019.8795769