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

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Drop, F. ; Pool, D. ; van Paassen, M. ; Mulder, M. ; Bülthoff, H. (2018)

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.
  • References (36)
    36 references, page 1 of 4

    [1] D. T. McRuer, D. Graham, E. S. Krendel, and W. J. Reisener, “Human Pilot Dynamics in Compensatory Systems, Theory Models and Experiments with Controlled Element and Forcing Function Variations,” Air Force Flight Dynamics Laboratory, Tech. Rep. AFFDL-TR-65-15, 1965.

    [2] E. R. Boer and R. V. Kenyon, “Estimation of Time-Varying Delay Time in Nonstationary Linear Systems: An Approach to Monitor Human Operator Adaptation in Manual Tracking Tasks,” IEEE Trans. on Systems, Man & Cybernetics - Part A: Systems and Humans, vol. 28, no. 1, pp. 89-99, 1998.

    [3] R. A. Hess, J. K. Moore, and M. Hubbard, “Modeling the Manually Controlled Bicycle,” IEEE Trans. on Systems, Man & Cybernetics, Part A: Systems and Humans, vol. 42, no. 3, pp. 545-557, 2012.

    [4] J. J. Potter and W. Singhose, “Improving Manual Tracking of Systems with Oscillatory Dynamics,” IEEE Trans. on Human-Machine Systems, vol. 43, no. 1, pp. 46-52, 2013.

    [5] K. Van der El, D. M. Pool, H. J. Damveld, M. M. Van Paassen, and M. Mulder, “An Empirical Human Controller Model for Preview Tracking Tasks,” IEEE Transactions on Cybernetics, Accepted for publication.

    [6] E. S. Krendel and D. T. McRuer, “A Servomechanics Approach to Skill Development,” J. of the Franklin Inst., vol. 269, no. 1, pp. 24-42, 1960.

    [7] R. J. Wasicko, D. T. McRuer, and R. E. Magdaleno, “Human Pilot Dynamic Response in Single-loop Systems with Compensatory and Pursuit Displays,” Air Force Flight Dynamics Laboratory, Tech. Rep. AFFDL-TR-66-137, 1966.

    [8] R. E. Magdaleno, H. R. Jex, and W. A. Johnson, “Tracking QuasiPredictable Displays Subjective Predictability Gradations, Pilot Models for Periodic and Narrowband Inputs,” in Fifth Annual Conf. on Manual Control, Cambridge (MA), March 27-29, 1969, pp. 391-428.

    [9] A. J. Bastian, “Learning to Predict the Future: the Cerebellum Adapts Feedforward Movement Control,” Current Opinion in Neurobiology, vol. 16, pp. 645-649, 2006.

    [10] A. J. Nagengast, D. A. Braun, and D. M. Wolpert, “Optimal Control Predicts Human Performance on Objects with Internal Degrees of Freedom,” PLOS Computational Biology, vol. 5, p. e1000419, 2009.

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