Investigation into Crossover Regression in Compensatory Manual Tracking Tasks

This paper investigates the crossover-regression phenomenon in compensatory manual-control tasks. The adjustment, between-subject variation, and accuracy of linear human-operator models are analyzed in detail. A theoretical investigation into closed-loop error minimization will be presented. Our mai...

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Bibliographic Details
Published in:Journal of guidance, control, and dynamics control, and dynamics, 2009-09, Vol.32 (5), p.1429-1445
Main Authors: Beerens, G. C, Damveld, H. J, Mulder, M, Van Paassen, M. M, Van Der Vaart, J. C
Format: Article
Language:English
Subjects:
Aerospace engineering
Applied sciences
Bandwidths
College professors
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Exact sciences and technology
Experiments
Hypotheses
Parameter estimation
Simulation
Software
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Summary:This paper investigates the crossover-regression phenomenon in compensatory manual-control tasks. The adjustment, between-subject variation, and accuracy of linear human-operator models are analyzed in detail. A theoretical investigation into closed-loop error minimization will be presented. Our main hypothesis was that crossover regression is caused by an operator's inability to sufficiently decrease the time delays required to limit forcing-function resonance. To test the hypothesis and explore the use of linear-operator models in regressed conditions, an experiment very similar to McRuer's landmark 1965 experiment was conducted. A comparison between regressive and nonregressive conditions revealed that crossover regression is indeed a strategy to reduce forcing-function resonance. The bandwidth of the forcing-function signal at which participants regressed their crossover frequency was found to vary considerably between participants. In regressed conditions, the between-subject variability in frequency-domain performance increased. Additionally, the operator control behavior became increasingly nonlinear, resulting in larger uncertainties and a higher between-subject variability in the linear-model parameter estimates. [PUBLISHER ABSTRACT]
ISSN:0731-5090
1533-3884
DOI:10.2514/1.43528