• DocumentCode
    728122
  • Title

    Frequency-domain observations on how humans learn to control an unknown dynamic system

  • Author

    Xingye Zhang ; Shaoqian Wang ; Seigler, T.M. ; Hoagg, Jesse B.

  • Author_Institution
    Dept. of Mech. Eng., Univ. of Kentucky, Lexington, KY, USA
  • fYear
    2015
  • fDate
    1-3 July 2015
  • Firstpage
    1143
  • Lastpage
    1148
  • Abstract
    This paper presents results from an experiment that is designed to explore the approaches that humans use to learn to control an unknown linear time-invariant dynamic system. In this experiment, 10 subjects interacted with an unknown dynamic system 40 times over a 2-week period. We use subsystem identification to model the control strategies that the subjects employ on each of their 40 trials. In particular, we estimate feedback and feedforward controllers used by each subject on each trial. The controllers identified on the 40th trial suggest that the subjects learned to use the inverse plant dynamics in feedforward. Moreover, the identified feedforward controllers converge to the approximate inverse dynamics in fewer trials (i.e., more quickly) at middle frequencies than at low and high frequencies.
  • Keywords
    estimation theory; feedback; feedforward; frequency-domain analysis; identification; linear systems; feedback estimation; feedforward controller; frequency-domain observation; inverse plant dynamics; subsystem identification; unknown linear time-invariant dynamic system; Adaptive control; Feedforward neural networks; Force; Frequency control; Standards; Time-domain analysis; Transfer functions;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference (ACC), 2015
  • Conference_Location
    Chicago, IL
  • Print_ISBN
    978-1-4799-8685-9
  • Type

    conf

  • DOI
    10.1109/ACC.2015.7170887
  • Filename
    7170887