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.
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