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Identification of the Feedforward Component of Manual Control in Tasks with Predictable Target Signals

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84423

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84644

Pool,  DM
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Drop, F., Pool, D., Damveld HJ, van Paassen, M., & Mulder, M. (2013). Identification of the Feedforward Component of Manual Control in Tasks with Predictable Target Signals. IEEE Transactions on Cybernetics, 43(6), 1936-1949. doi:10.1109/TSMCB.2012.2235829.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-001A-123F-2
Zusammenfassung
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.