Extended Crossover Model for Human-Control of Fractional Order Plants

A data-driven generalization of the crossover model is proposed, characterizing the human control of systems with both integer and fractional-order plant dynamics. The model is developed and validated using data obtained from human subjects operating in compensatory and pursuit tracking tasks. From...

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Main Authors: Miguel Martinez-Garcia, Timothy Gordon, Lei Shu
Format: Article
Language:English
Published: IEEE 2017-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8120155/
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spelling doaj-2a5b05c6dd894ddfbca4d2bdbae789182021-03-29T20:19:02ZengIEEEIEEE Access2169-35362017-01-015276222763510.1109/ACCESS.2017.27780138120155Extended Crossover Model for Human-Control of Fractional Order PlantsMiguel Martinez-Garcia0https://orcid.org/0000-0003-2984-3231Timothy Gordon1Lei Shu2School of Engineering, University of Lincoln, Lincoln, U.K.School of Engineering, University of Lincoln, Lincoln, U.K.School of Engineering, University of Lincoln, Lincoln, U.K.A data-driven generalization of the crossover model is proposed, characterizing the human control of systems with both integer and fractional-order plant dynamics. The model is developed and validated using data obtained from human subjects operating in compensatory and pursuit tracking tasks. From the model, it is inferred that humans possess a limited but consistent capability to compensate for fractional-order plant dynamics. Further, a review of potential sources of fractionality within such man-machine systems suggests that visual perception, based on visual cues that contain memory, and muscular dynamics are likely sources of fractional-order dynamics within humans themselves. Accordingly, a possible mechanism for fractional-order compensation, operating between visual and muscular subsystems, is proposed. Deeper analysis of the data shows that human response is more highly correlated to fractional-order representations of visual cues, rather than directly to objective engineering variables, as is commonly proposed in human control models in the literature. These results are expected to underpin future design developments in human-in-the-loop cyber-physical systems, for example, in semi-autonomous highway driving.https://ieeexplore.ieee.org/document/8120155/Human-in-the-loop systemsdata-driven modelingcyber-physical systems oriented controlfractional order controlvehicle automation
collection DOAJ
language English
format Article
sources DOAJ
author Miguel Martinez-Garcia
Timothy Gordon
Lei Shu
spellingShingle Miguel Martinez-Garcia
Timothy Gordon
Lei Shu
Extended Crossover Model for Human-Control of Fractional Order Plants
IEEE Access
Human-in-the-loop systems
data-driven modeling
cyber-physical systems oriented control
fractional order control
vehicle automation
author_facet Miguel Martinez-Garcia
Timothy Gordon
Lei Shu
author_sort Miguel Martinez-Garcia
title Extended Crossover Model for Human-Control of Fractional Order Plants
title_short Extended Crossover Model for Human-Control of Fractional Order Plants
title_full Extended Crossover Model for Human-Control of Fractional Order Plants
title_fullStr Extended Crossover Model for Human-Control of Fractional Order Plants
title_full_unstemmed Extended Crossover Model for Human-Control of Fractional Order Plants
title_sort extended crossover model for human-control of fractional order plants
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2017-01-01
description A data-driven generalization of the crossover model is proposed, characterizing the human control of systems with both integer and fractional-order plant dynamics. The model is developed and validated using data obtained from human subjects operating in compensatory and pursuit tracking tasks. From the model, it is inferred that humans possess a limited but consistent capability to compensate for fractional-order plant dynamics. Further, a review of potential sources of fractionality within such man-machine systems suggests that visual perception, based on visual cues that contain memory, and muscular dynamics are likely sources of fractional-order dynamics within humans themselves. Accordingly, a possible mechanism for fractional-order compensation, operating between visual and muscular subsystems, is proposed. Deeper analysis of the data shows that human response is more highly correlated to fractional-order representations of visual cues, rather than directly to objective engineering variables, as is commonly proposed in human control models in the literature. These results are expected to underpin future design developments in human-in-the-loop cyber-physical systems, for example, in semi-autonomous highway driving.
topic Human-in-the-loop systems
data-driven modeling
cyber-physical systems oriented control
fractional order control
vehicle automation
url https://ieeexplore.ieee.org/document/8120155/
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AT timothygordon extendedcrossovermodelforhumancontroloffractionalorderplants
AT leishu extendedcrossovermodelforhumancontroloffractionalorderplants
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