The Impact of Command-Following Task on Human-in-the-Loop Control Behavior

S. Alireza Seyyed Mousavi, Faina Matveeva, Xingye Zhang, T. Michael Seigler, Jesse B. Hoagg

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This article presents results from an experiment in which 44 human subjects interact with a dynamic system 40 times over a one-week period. The subjects are divided into four groups. All groups interact with the same dynamic system, but each group performs a different sequence of command-following tasks. All reference commands have frequency content between 0 and 0.5 Hz. We use a subsystem identification algorithm to estimate the control strategy (feedback and feedforward) that each subject uses on each trial. The experimental and identification results are used to examine the impact of the command-following tasks on the subjects' performance and the control strategies that the subjects learn. Results demonstrate that certain reference commands (e.g., a sum of sinusoids) are more difficult for subjects to learn to follow than others (e.g., a chirp), and the difference in difficulty is related to the subjects' ability to match the phase of the reference command. In addition, the identification results show that differences in command-following performance for different tasks can be attributed to three aspects of the subjects' identified controllers: 1) compensating for time delay in feedforward; 2) using a comparatively accurate approximation of the inverse dynamics in feedforward; and 3) using a feedback controller with comparatively high gain. Results also demonstrate that subjects generalize their control strategy when the command changes. Specifically, when the command changes, subjects maintain relatively high gain in feedback and retain their feedforward internal model of the inverse dynamics. Finally, we provide evidence that subjects use prediction of the command (if possible) to improve performance but that subjects can learn to improve performance without prediction. Specifically, subjects learn to use feedback controllers with comparatively high gain to improve performance even though the command is unpredictable.

Original languageEnglish
Pages (from-to)6447-6461
Number of pages15
JournalIEEE Transactions on Cybernetics
Volume52
Issue number7
DOIs
StatePublished - Jul 1 2022

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Generalization
  • human control behavior
  • learning
  • prediction
  • subsystem identification (SSID)
  • task

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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