Title :
Identification of the Feedforward Component in Manual Control With Predictable Target Signals
Author :
Drop, Frank M. ; Pool, Daan M. ; Damveld, Herman J. ; van Paassen, Marinus M. ; Mulder, Max
Author_Institution :
Max Planck Inst. for Biol. Cybern., Tubingen, Germany
Abstract :
In the manual control of a dynamic system, the human controller (HC) often follows a visible and predictable reference path. Compared with a purely feedback control strategy, performance can be improved by making use of this knowledge of the reference. The operator could effectively introduce feedforward control in conjunction with a feedback path to compensate for errors, as hypothesized in literature. However, feedforward behavior has never been identified from experimental data, nor have the hypothesized models been validated. This paper investigates human control behavior in pursuit tracking of a predictable reference signal while being perturbed by a quasi-random multisine disturbance signal. An experiment was done in which the relative strength of the target and disturbance signals were systematically varied. The anticipated changes in control behavior were studied by means of an ARX model analysis and by fitting three parametric HC models: two different feedback models and a combined feedforward and feedback model. The ARX analysis shows that the experiment participants employed control action on both the error and the target signal. The control action on the target was similar to the inverse of the system dynamics. Model fits show that this behavior can be modeled best by the combined feedforward and feedback model.
Keywords :
autoregressive processes; compensation; control system analysis; feedback; feedforward; ARX model analysis; autoregressive with exogeneous input; combined feedforward-feedback model; error compensation; feedback path; feedforward component identification; feedforward control; human controller; manual control; predictable target signals; purely feedback control strategy; quasirandom multisine disturbance signal; Analytical models; Feedforward neural networks; Human factors; Humans; Predictive models; Stability analysis; Target tracking; Feedforward; manual control; precognitive control; pursuit; tracking tasks;
Journal_Title :
Cybernetics, IEEE Transactions on
DOI :
10.1109/TSMCB.2012.2235829