文章基本信息

标题：Experimental Scheduling Functions for Global LPV Human Controller Modeling
本地全文：下载
作者：R.F.M. Duarte ; D.M. Pool ; M.M. van Paassen 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：15853-15858
DOI：10.1016/j.ifacol.2017.08.2329
语种：English
出版社：Elsevier
摘要：AbstractIn 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.
关键词：KeywordsHuman-machine systemsManual controlTime-varying systemsSystem identificationLPV models