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arXiv · 2002.09627

Feedback for nonlinear system identification

Thiago B. Burghi·Maarten Schoukens·Rodolphe Sepulchre
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Abstract

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.

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Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre. 2020-02-22. Feedback for nonlinear system identification. https://doi.org/10.23919/ecc.2019.8795769

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