A subsystem identification technique for modeling control strategies used by humans

Xingye Zhang; Shaoqian Wang; T. M. Seigler; Jesse B. Hoagg

doi:10.1109/ACC.2014.6859211

A subsystem identification technique for modeling control strategies used by humans

Xingye Zhang, Shaoqian Wang, T. M. Seigler, Jesse B. Hoagg

Mechanical and Aerospace Engineering

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

10 Scopus citations

Abstract

This paper presents evidence in support of the internal model hypothesis of neuroscience. Specifically, we present results from a study that includes 10 human subjects and is designed to explore the internal model hypothesis. A new system identification method is presented for composite systems that include multiple unknown subsystems whose input and output signals may be inaccessible (i.e., unmeasurable). We use this subsystem identification method to model the control strategies that humans employ. In particular, we identify the feedback and feedforward controllers of the subjects in the experiment. The identified controllers suggest that the subjects learned to use inverse plant dynamics in feedforward.

Original language	English
Title of host publication	2014 American Control Conference, ACC 2014
Pages	2827-2832
Number of pages	6
DOIs	https://doi.org/10.1109/ACC.2014.6859211
State	Published - 2014
Event	2014 American Control Conference, ACC 2014 - Portland, OR, United States Duration: Jun 4 2014 → Jun 6 2014

Publication series

Name	Proceedings of the American Control Conference
ISSN (Print)	0743-1619

Conference

Conference	2014 American Control Conference, ACC 2014
Country/Territory	United States
City	Portland, OR
Period	6/4/14 → 6/6/14

Keywords

Behavioral systems
Biologically-inspired methods
Identification

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2014.6859211

Cite this

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title = "A subsystem identification technique for modeling control strategies used by humans",

abstract = "This paper presents evidence in support of the internal model hypothesis of neuroscience. Specifically, we present results from a study that includes 10 human subjects and is designed to explore the internal model hypothesis. A new system identification method is presented for composite systems that include multiple unknown subsystems whose input and output signals may be inaccessible (i.e., unmeasurable). We use this subsystem identification method to model the control strategies that humans employ. In particular, we identify the feedback and feedforward controllers of the subjects in the experiment. The identified controllers suggest that the subjects learned to use inverse plant dynamics in feedforward.",

keywords = "Behavioral systems, Biologically-inspired methods, Identification",

author = "Xingye Zhang and Shaoqian Wang and Seigler, {T. M.} and Hoagg, {Jesse B.}",

year = "2014",

doi = "10.1109/ACC.2014.6859211",

language = "English",

isbn = "9781479932726",

series = "Proceedings of the American Control Conference",

pages = "2827--2832",

booktitle = "2014 American Control Conference, ACC 2014",

note = "2014 American Control Conference, ACC 2014 ; Conference date: 04-06-2014 Through 06-06-2014",

}

Zhang, X, Wang, S, Seigler, TM & Hoagg, JB 2014, A subsystem identification technique for modeling control strategies used by humans. in 2014 American Control Conference, ACC 2014., 6859211, Proceedings of the American Control Conference, pp. 2827-2832, 2014 American Control Conference, ACC 2014, Portland, OR, United States, 6/4/14. https://doi.org/10.1109/ACC.2014.6859211

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AU - Wang, Shaoqian

AU - Seigler, T. M.

AU - Hoagg, Jesse B.

PY - 2014

Y1 - 2014

N2 - This paper presents evidence in support of the internal model hypothesis of neuroscience. Specifically, we present results from a study that includes 10 human subjects and is designed to explore the internal model hypothesis. A new system identification method is presented for composite systems that include multiple unknown subsystems whose input and output signals may be inaccessible (i.e., unmeasurable). We use this subsystem identification method to model the control strategies that humans employ. In particular, we identify the feedback and feedforward controllers of the subjects in the experiment. The identified controllers suggest that the subjects learned to use inverse plant dynamics in feedforward.

AB - This paper presents evidence in support of the internal model hypothesis of neuroscience. Specifically, we present results from a study that includes 10 human subjects and is designed to explore the internal model hypothesis. A new system identification method is presented for composite systems that include multiple unknown subsystems whose input and output signals may be inaccessible (i.e., unmeasurable). We use this subsystem identification method to model the control strategies that humans employ. In particular, we identify the feedback and feedforward controllers of the subjects in the experiment. The identified controllers suggest that the subjects learned to use inverse plant dynamics in feedforward.

KW - Behavioral systems

KW - Biologically-inspired methods

KW - Identification

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DO - 10.1109/ACC.2014.6859211

M3 - Conference contribution

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T3 - Proceedings of the American Control Conference

SP - 2827

EP - 2832

BT - 2014 American Control Conference, ACC 2014

T2 - 2014 American Control Conference, ACC 2014

Y2 - 4 June 2014 through 6 June 2014

ER -

A subsystem identification technique for modeling control strategies used by humans

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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