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  Constraints in Identification of Multi-Loop Feedforward Human Control Models

Drop, F., Pool, D., Mulder, M., & Bülthoff, H. (2016). Constraints in Identification of Multi-Loop Feedforward Human Control Models. IFAC-PapersOnLine, 49(19), 7-12.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-7A7C-D Version Permalink: http://hdl.handle.net/21.11116/0000-0003-89E3-2
Genre: Conference Paper

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 Creators:
Drop, FM1, 2, 3, 4, Author              
Pool, DM4, Author              
Mulder, M, Author
Bülthoff, HH1, 3, 4, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Project group: Motion Perception & Simulation, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528705              
3Project group: Cybernetics Approach to Perception & Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528701              
4Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              

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 Abstract: The human controller (HC) can greatly improve target-tracking performance by utilizing a feedforward operation on the target signal, in addition to a feedback response. System identification methods are used to determine the correct HC model structure: purely feedback or a combined feedforward/feedback model. In this paper, we investigate three central issues that complicate this objective. First, the identification method should not require prior assumptions regarding the dynamics of the feedforward and feedback components. Second, severe biases might be introduced by high levels of noise in the data measured under closed-loop conditions. To address the first two issues, we will consider two identification methods that make use of linear ARX models: the classic direct method and the two-stage indirect method of van den Hof and Schrama (1993). Third, model complexity should be considered in the selection of the ‘best’ ARX model to prevent ‘false-positive’ feedforward identification. Various model selection criteria, that make an explicit trade-off between model quality and model complexity, are considered. Based on computer simulations with a HC model, we conclude that 1) the direct method provides more accurate estimates in the frequency range of interest, and 2) existing model selection criteria do not prevent false-positive feedforward identification.

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 Dates: 2016-08
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1016/j.ifacol.2016.10.444
BibTex Citekey: DropPMB2016
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Title: 13th IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems (HMS 2016)
Place of Event: Kyoto, Japan
Start-/End Date: 2016-08-30 - 2016-09-02

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Title: IFAC-PapersOnLine
  Other : IFAC Papers Online
Source Genre: Journal
 Creator(s):
Sawaragi, T., Editor
Affiliations:
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Publ. Info: Frankfurt ; München [u.a.] : Elsevier
Pages: - Volume / Issue: 49 (19) Sequence Number: - Start / End Page: 7 - 12 Identifier: ISSN: 2405-8963
CoNE: https://pure.mpg.de/cone/journals/resource/2405-8963