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  • 标题:UKF-based Identification of Time-Varying Manual Control Behaviour
  • 本地全文:下载
  • 作者:Jim Rojer ; Daan M. Pool ; Marinus M. van Paassen
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2019
  • 卷号:52
  • 期号:19
  • 页码:109-114
  • DOI:10.1016/j.ifacol.2019.12.120
  • 语种:English
  • 出版社:Elsevier
  • 摘要:This paper describes a novel method for time-varying identification of Human Controller (HC) manual control parameters (called UKF-FPV), based on a steady-state (constant state covariance) Unscented Kalman Filter (UKF). This approach requires noa prioriassumptions on the shape of HC parameter variations, which is a potential advantage over state-of-the-art methods such as the recently proposed MLE-APV approach, for which a sigmoid-shaped parameter variation is assumed. For a scenario where an HC performs a single-loop compensatory tracking task with time-varying controlled system dynamics, both identification methods are compared using Monte Carlo simulations and human-in-the-loop experiment data. Despite some lag in the HC parameter traces of UKF-FPV, the identification results and the HC model quality-of-fit obtained with both methods were found to match well for both the simulation and experiment data. For the experiment data, UKF-FPV even revealed clear "local" changes in HC parameters not captured by the MLE-APV approach, which confirms that HCs adaptunpredictablyeven in what are considered time-invariant conditions. Overall, the results show that an identification method that requires noa prioriassumptions on HC parameter variations is of critical importance for a complete analysis of time-varying HC behaviour.
  • 关键词:KeywordsCyberneticsmanual controltime-varying identificationUnscented Kalman Filter
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