System identification in the presence of nonlinear sensors

Shuichi Adachi; Yasushi Okada; Jan M. Maciejowski

doi:10.1016/S1474-6670(17)31465-9

System identification in the presence of nonlinear sensors

Shuichi Adachi, Yasushi Okada, Jan M. Maciejowski

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

Abstract

We consider a system identification problem in which input and output signals are measured using sensors with static nonlinear characteristics, such as saturation, dead zone, and so on. A new system identification method which estimates the sensor errors due to nonlinearity, as well as system parameters, is proposed. the key idea is to formulate the identification problem as a constrained optimization problem. the system parameters are then estimated using a quasi-Newton method. Effectiveness of the proposed method is examined through numerical experiments.

Original language	English
Pages (from-to)	185-190
Number of pages	6
Journal	IFAC Proceedings Volumes (IFAC-PapersOnline)
Volume	37
Issue number	12
DOIs	https://doi.org/10.1016/S1474-6670(17)31465-9
Publication status	Published - 2004
Externally published	Yes
Event	2004 IFAC Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2004 and IFAC Workshop on Periodic Control Systems, PSYCO 2004 - Yokohama, Japan Duration: 2004 Aug 30 → 2004 Sept 1

Keywords

Constrained optimization problem
Modeling
System identification

ASJC Scopus subject areas

Control and Systems Engineering

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10.1016/S1474-6670(17)31465-9

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title = "System identification in the presence of nonlinear sensors",

abstract = "We consider a system identification problem in which input and output signals are measured using sensors with static nonlinear characteristics, such as saturation, dead zone, and so on. A new system identification method which estimates the sensor errors due to nonlinearity, as well as system parameters, is proposed. the key idea is to formulate the identification problem as a constrained optimization problem. the system parameters are then estimated using a quasi-Newton method. Effectiveness of the proposed method is examined through numerical experiments.",

keywords = "Constrained optimization problem, Modeling, System identification",

author = "Shuichi Adachi and Yasushi Okada and Maciejowski, {Jan M.}",

note = "Publisher Copyright: Copyright {\textcopyright} 2004 IFAC; 2004 IFAC Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2004 and IFAC Workshop on Periodic Control Systems, PSYCO 2004 ; Conference date: 30-08-2004 Through 01-09-2004",

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doi = "10.1016/S1474-6670(17)31465-9",

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TY - JOUR

T1 - System identification in the presence of nonlinear sensors

AU - Adachi, Shuichi

AU - Okada, Yasushi

AU - Maciejowski, Jan M.

PY - 2004

Y1 - 2004

N2 - We consider a system identification problem in which input and output signals are measured using sensors with static nonlinear characteristics, such as saturation, dead zone, and so on. A new system identification method which estimates the sensor errors due to nonlinearity, as well as system parameters, is proposed. the key idea is to formulate the identification problem as a constrained optimization problem. the system parameters are then estimated using a quasi-Newton method. Effectiveness of the proposed method is examined through numerical experiments.

AB - We consider a system identification problem in which input and output signals are measured using sensors with static nonlinear characteristics, such as saturation, dead zone, and so on. A new system identification method which estimates the sensor errors due to nonlinearity, as well as system parameters, is proposed. the key idea is to formulate the identification problem as a constrained optimization problem. the system parameters are then estimated using a quasi-Newton method. Effectiveness of the proposed method is examined through numerical experiments.

KW - Constrained optimization problem

KW - Modeling

KW - System identification

UR - http://www.scopus.com/inward/record.url?scp=85064413416&partnerID=8YFLogxK

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U2 - 10.1016/S1474-6670(17)31465-9

DO - 10.1016/S1474-6670(17)31465-9

M3 - Conference article

AN - SCOPUS:85064413416

SN - 2405-8963

VL - 37

SP - 185

EP - 190

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 12

T2 - 2004 IFAC Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2004 and IFAC Workshop on Periodic Control Systems, PSYCO 2004

Y2 - 30 August 2004 through 1 September 2004

ER -

System identification in the presence of nonlinear sensors

Abstract

Keywords

ASJC Scopus subject areas

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