Feedback for nonlinear system identification

Thiago Burghi; Maarten Schoukens; Rodolphe Sepulchre

doi:10.23919/ECC.2019.8795769

Feedback for nonlinear system identification

Thiago Burghi
, Maarten Schoukens
, Rodolphe Sepulchre

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

4 Citations (Scopus)

3 Downloads (Pure)

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
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	https://doi.org/10.23919/ECC.2019.8795769
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	https://www.ifac-control.org/events/european-control-conference-in-cooperation-with-ifac-ecc-2019

Funding

*The research leading to these results has received funding from the European Research Council under the Advanced ERC Grant Agreement Switchlet n.670645 and from the Brazilian federal agency for the Coordination of Improvement of Higher Education Personnel (CAPES).

Keywords

Approximately-finite memory
Excitability
Nonlinear systems
Output feedback
Systems identification

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10.23919/ECC.2019.8795769

Cite this

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title = "Feedback for nonlinear system identification",

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.",

keywords = "Approximately-finite memory, Excitability, Nonlinear systems, Output feedback, Systems identification",

author = "Thiago Burghi and Maarten Schoukens and Rodolphe Sepulchre",

year = "2019",

month = jun,

day = "1",

doi = "10.23919/ECC.2019.8795769",

language = "English",

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booktitle = "2019 18th European Control Conference, ECC 2019",

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address = "United States",

note = "18th European Control Conference, ECC 2019, ECC 2019 ; Conference date: 25-06-2019 Through 28-06-2019",

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AU - Schoukens, Maarten

AU - Sepulchre, Rodolphe

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Y1 - 2019/6/1

N2 - 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.

AB - 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.

KW - Approximately-finite memory

KW - Excitability

KW - Nonlinear systems

KW - Output feedback

KW - Systems identification

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DO - 10.23919/ECC.2019.8795769

M3 - Conference contribution

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EP - 1349

BT - 2019 18th European Control Conference, ECC 2019

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 18th European Control Conference, ECC 2019

Y2 - 25 June 2019 through 28 June 2019

ER -

Feedback for nonlinear system identification

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