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  Modeling Human Multichannel Perception and Control Using Linear Time-Invariant Models

Nieuwenhuizen, F., Zaal, P., Mulder, M., van Paassen, M., & Mulder, J. (2008). Modeling Human Multichannel Perception and Control Using Linear Time-Invariant Models. Journal of Guidance, Control, and Dynamics, 31(4), 999-1013. doi:10.2514/1.32307.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-302D-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-302E-4
Genre: Journal Article

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https://arc.aiaa.org/doi/10.2514/1.32307 (Publisher version)
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 Creators:
Nieuwenhuizen, FM1, 2, Author              
Zaal, PMT, Author
Mulder, M, Author
van Paassen, MM, Author
Mulder , JA, Author
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: This paper introduces a two-step identification method of human multichannel perception and control. In the first step, frequency response functions are identified using linear time-invariant models. The analytical predictions of bias and variance in the estimated frequency response functions are validated using Monte Carlo simulations of a closed-loop control task and contrasted to a conventional method using Fourier coefficients. For both methods, the analytical predictions are reliable, but the linear time-invariant method has lower bias and variance than Fourier coefficients. It is further shown that the linear time-invariant method is more robust to higher levels of pilot remnant. Finally, both methods were successfully applied to experimental data from closed-loop control tasks with pilots. Modeling Human Multichannel Perception and Control Using Linear Time-Invariant Models (PDF Download Available).

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 Dates: 2008-07
 Publication Status: Published in print
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 Identifiers: DOI: 10.2514/1.32307
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Title: Journal of Guidance, Control, and Dynamics
Source Genre: Journal
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Pages: - Volume / Issue: 31 (4) Sequence Number: - Start / End Page: 999 - 1013 Identifier: -