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
Link To Document