Frequency-domain subsystem identification with application to modeling human control behavior

Xingye Zhang; Jesse B. Hoagg

doi:10.1016/j.sysconle.2015.10.009

Frequency-domain subsystem identification with application to modeling human control behavior

Xingye Zhang, Jesse B. Hoagg

Mechanical and Aerospace Engineering

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

We present a frequency-domain subsystem identification algorithm that identifies unknown feedback and feedforward subsystems that are interconnected with a known subsystem. This method requires only accessible input and output measurements, applies to linear time-invariant subsystems, and uses a candidate-pool approach to ensure asymptotic stability of the identified closed-loop transfer function. We analyze the algorithm in the cases of noiseless and noisy data. The main analytic result of this paper shows that the coefficients of the identified feedback and feedforward transfer functions are arbitrarily close to the true coefficients if the data noise is sufficiently small and the candidate pool is sufficiently dense. This subsystem identification approach has application to modeling the control behavior of humans interacting with and receiving feedback from a dynamic system. We apply the algorithm to data from a human-in-the-loop experiment to model a human's control behavior.

Original language	English
Pages (from-to)	36-46
Number of pages	11
Journal	Systems and Control Letters
Volume	87
DOIs	https://doi.org/10.1016/j.sysconle.2015.10.009
State	Published - Jan 1 2016

Bibliographical note

Publisher Copyright:
© 2015 Elsevier B.V.

Funding

This work is supported by the National Science Foundation (award number: CMMI–1405257 ) and the Kentucky Science and Engineering Foundation (award number: KSEF-148-502-12-288 ). We also want to acknowledge Dr. Sheetz and Mr. Gazula from the Center for Computational Sciences at the University of Kentucky for providing assistance in coding Algorithm 1 for parallel processing.

Funders	Funder number
National Science Foundation (NSF)	CMMI–1405257, 1405257
Kentucky Science and Engineering Foundation	KSEF-148-502-12-288

Keywords

Human motor control
Human-in-the-loop
Subsystem identification

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science
Mechanical Engineering
Electrical and Electronic Engineering

Access to Document

10.1016/j.sysconle.2015.10.009

Cite this

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title = "Frequency-domain subsystem identification with application to modeling human control behavior",

abstract = "We present a frequency-domain subsystem identification algorithm that identifies unknown feedback and feedforward subsystems that are interconnected with a known subsystem. This method requires only accessible input and output measurements, applies to linear time-invariant subsystems, and uses a candidate-pool approach to ensure asymptotic stability of the identified closed-loop transfer function. We analyze the algorithm in the cases of noiseless and noisy data. The main analytic result of this paper shows that the coefficients of the identified feedback and feedforward transfer functions are arbitrarily close to the true coefficients if the data noise is sufficiently small and the candidate pool is sufficiently dense. This subsystem identification approach has application to modeling the control behavior of humans interacting with and receiving feedback from a dynamic system. We apply the algorithm to data from a human-in-the-loop experiment to model a human's control behavior.",

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AU - Hoagg, Jesse B.

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AB - We present a frequency-domain subsystem identification algorithm that identifies unknown feedback and feedforward subsystems that are interconnected with a known subsystem. This method requires only accessible input and output measurements, applies to linear time-invariant subsystems, and uses a candidate-pool approach to ensure asymptotic stability of the identified closed-loop transfer function. We analyze the algorithm in the cases of noiseless and noisy data. The main analytic result of this paper shows that the coefficients of the identified feedback and feedforward transfer functions are arbitrarily close to the true coefficients if the data noise is sufficiently small and the candidate pool is sufficiently dense. This subsystem identification approach has application to modeling the control behavior of humans interacting with and receiving feedback from a dynamic system. We apply the algorithm to data from a human-in-the-loop experiment to model a human's control behavior.

KW - Human motor control

KW - Human-in-the-loop

KW - Subsystem identification

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Frequency-domain subsystem identification with application to modeling human control behavior

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

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