Experimental Scheduling Functions for Global LPV Human Controller Modeling

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Abstract

In this paper, the Linear Parameter Varying (LPV) model identification framework is applied to estimating time-varying human controller (HC) dynamics in a single-loop tracking task. Given the inherently unknown time changes in HC behavior, a global LPV approach with experimentally determined Scheduling Functions (SFs) is needed for this application. In this paper, a methodology based on the Predictor-Based Subspace Identification (PBSID) algorithm is tested. Using Monte Carlo simulation data matching a recent experimental study, two experimental SFs derived from measured HC control inputs are tested for their LPV model identification performance. The results are compared with LPV models obtained using the true (analytical) SFs used for generating the simulation data. An experimental SF obtained from the double derivative of HCs’ control inputs using zero-phase low-pass filtering was found to yield time-varying HC model estimates of equivalent accuracy as obtained with the analytical SFs; a promising result for future application of this methodology to measured HC behavior.
Original languageEnglish
Title of host publication20h World Congress of the International Federation of Automatic Control (IFAC), 2017
EditorsD. Dochain, D. Henrion, D. Peaucelle
PublisherElsevier
Pages15853-15858
Volume50
Edition1
DOIs
Publication statusE-pub ahead of print - 2017
Event20th World Congress of the International Federation of Automatic Control (IFAC), 2017 - Toulouse, France
Duration: 9 Jul 201714 Jul 2017
Conference number: 20
https://www.ifac2017.org

Publication series

NameIFAC-PapersOnline
Number1
Volume50
ISSN (Print)2405-8963

Conference

Conference20th World Congress of the International Federation of Automatic Control (IFAC), 2017
Abbreviated titleIFAC 2017
CountryFrance
CityToulouse
Period9/07/1714/07/17
Internet address

Keywords

  • Human-machine systems
  • Manual control
  • Time-varying systems;
  • System identification
  • LPV models

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  • Cite this

    Duarte, R. F. M., Pool, D., van Paassen, R., & Mulder, M. (2017). Experimental Scheduling Functions for Global LPV Human Controller Modeling. In D. Dochain, D. Henrion, & D. Peaucelle (Eds.), 20h World Congress of the International Federation of Automatic Control (IFAC), 2017 (1 ed., Vol. 50, pp. 15853-15858). (IFAC-PapersOnline; Vol. 50, No. 1). Elsevier. https://doi.org/10.1016/j.ifacol.2017.08.2329