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.
| Original language | English |
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| Title of host publication | 2019 18th European Control Conference, ECC 2019 |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 1344-1349 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-3-907144-00-8 |
| DOIs | |
| Publication status | Published - 1 Jun 2019 |
| Event | 18th European Control Conference, ECC 2019 - Naples, Italy, Naples, Italy Duration: 25 Jun 2019 → 28 Jun 2019 Conference number: 18 https://www.ifac-control.org/events/european-control-conference-in-cooperation-with-ifac-ecc-2019 |
Conference
| Conference | 18th European Control Conference, ECC 2019 |
|---|---|
| Abbreviated title | ECC 2019 |
| Country/Territory | Italy |
| City | Naples |
| Period | 25/06/19 → 28/06/19 |
| Other | 18th European Control Conference (ECC 2019) (in cooperation with IFAC) |
| Internet address |
Keywords
- Approximately-finite memory
- Excitability
- Nonlinear systems
- Output feedback
- Systems identification