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

• Published in: IEEE transactions on cybernetics Vol. 48; no. 1; pp. 2 - 15
• Main Authors: Drop, Frank M., Pool, Daan M., van Paassen, Marinus Rene M., Mulder, Max, Bulthoff, Heinrich H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-01-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aircraft; Autoregressive models; Bayesian analysis; Complexity theory; Computational modeling; Computer simulation; Control systems; Control tasks; Control theory; Criteria; Data models; Feedback control; Feedforward control; Feedforward neural networks; Human behavior; human control models; Human factors; Identification methods; Manual control; Object recognition; Order parameters; parameter estimation; System identification; Weighting
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2016.2602322

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.