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Identifying Time-Varying Pilot Responses: A Regularized Recursive Least-Squares Algorithm

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Olivari,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Cybernetics Approach to Perception & Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Motion Perception & Simulation, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Venrooij,  J
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Nieuwenhuizen,  FM
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bülthoff,  HH
Project group: Cybernetics Approach to Perception & Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Olivari, M., Venrooij, J., Nieuwenhuizen, F., Pollini, L., & Bülthoff, H. (2016). Identifying Time-Varying Pilot Responses: A Regularized Recursive Least-Squares Algorithm. In AIAA Modeling and Simulation Technologies Conference: Held at the AIAA SciTech Forum 2016 (pp. 385-399). Red Hook, NY, USA: Curran.


Cite as: http://hdl.handle.net/21.11116/0000-0000-7AAA-8
Abstract
Methods for identifying pilot's responses commonly assume time-invariant dynamics. However, humans are likely to vary their responses during realistic control scenarios. In this work an identification method is developed for estimating time-varying responses to visual and force feedback during a compensatory tracking task. The method describes pilot's responses with finite impulse response filters and use a Regularized Recursive Least Squares (RegRLS) algorithm to simultaneously estimate filter coefficients. The method was validated in a Monte-Carlo simulation study with different levels of remnant noise. With low levels of remnant noise, estimates were accurate and tracked the time-varying behaviour of the simulated responses. On the other hand, estimates showed high variability in case of large remnant noise. However, parameters of the RegRLS could be further optimized to improve robustness to large remnant noise. Taken together, these findings suggest that the novel RegRLS algorithm could be used to estimate time-varying pilot's responses in real human-in-the-loop experiments.