Identifying Human Preview Control Behavior Using Subsystem Identification

Pieter Bas J.C. Bentinck, Daan M. Pool, Kasper van der El, Jesse B. Hoagg, Max Mulder

Research output: Contribution to journalConference articlepeer-review


Better understanding of manual control requires more research on human anticipatory feedforward behaviour. Recent advances include a human control model for preview tracking, and a subsystem identification (SSID) technique that uses a candidate pool approach to identify the human feedforward and feedback responses. This paper discusses the performance of the SSID method when estimating the preview control model parameters. Through simulations of a preview task with two controlled element dynamics, the SSID performance with different remnant noise levels and candidate pool densities is quantified. We demonstrate its successful application to the preview model and show that its performance deteriorates for higher noise levels. While the feedforward parameters are estimated accurately, the high-frequency compensatory feedback dynamics cannot be reliably determined. Future work focuses on alternative formulations for using SSID to estimate preview model parameters. Since in manual control the closed-loop magnitude decreases at higher frequencies, effects of manipulating the weightings of the closed-loop fitting cost values at these frequencies must be further analyzed.

Original languageEnglish
Pages (from-to)172-177
Number of pages6
Issue number29
StatePublished - 2022
Event15th IFAC Symposium on Analysis, Design and Evaluation of Human Machine Systems, HMS 2022 - San Jose, United States
Duration: Sep 12 2022Sep 15 2022

Bibliographical note

Publisher Copyright:
Copyright © 2022 The Authors. This is an open access article under the CC BY-NC-ND license (


  • Modeling of HMS
  • Modeling of human performance

ASJC Scopus subject areas

  • Control and Systems Engineering


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