Objective Model Selection for Identifying the Human Feedforward Response in Manual Control

Journal Article (2017)
Author(s)

F. M. Drop (TU Delft - Control & Simulation)

D.M. Pool (TU Delft - Control & Simulation)

MM van Paassen (TU Delft - Control & Simulation)

M Mulder (TU Delft - Control & Operations)

Heinrich H. Bülthoff (Max Planck Institute for Biological Cybernetics)

Research Group
Control & Simulation
Copyright
© 2017 F.M. Drop, D.M. Pool, M.M. van Paassen, Max Mulder, Heinrich H. Bülthoff
DOI related publication
https://doi.org/10.1109/TCYB.2016.2602322
More Info
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Publication Year
2017
Language
English
Copyright
© 2017 F.M. Drop, D.M. Pool, M.M. van Paassen, Max Mulder, Heinrich H. Bülthoff
Research Group
Control & Simulation
Issue number
1
Volume number
48 (2018)
Pages (from-to)
2-15
Reuse Rights

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Abstract

Realistic manual control tasks typically involve predictable target signals and random disturbances. The human controller (HC) is hypothesized to use a feedforward control strategy for target-following, in addition to feedback control for disturbance-rejection. Little is known about human feedforward control, partly because common system identification methods have difficulty in identifying whether, and (if so) how, the HC applies a feedforward strategy. In this paper, an identification procedure is presented that aims at an objective model selection for identifying the human feedforward response, using linear time-invariant autoregressive with exogenous input models. A new model selection criterion is proposed to decide on the model order (number of parameters) and the presence of feedforward in addition to feedback. For a range of typical control tasks, it is shown by means of Monte Carlo computer simulations that the classical Bayesian information criterion (BIC) leads to selecting models that contain a feedforward path from data generated by a pure feedback model: “false-positive” feedforward detection. To eliminate these false-positives, the modified BIC includes an additional penalty on model complexity. The appropriate weighting is found through computer simulations with a hypothesized HC model prior to performing a tracking experiment. Experimental human-in-the-loop data will be considered in future work. With appropriate weighting, the method correctly identifies the HC dynamics in a wide range of control tasks, without false-positive results.

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