Operator Performance Prediction based on Fuzzy Modeling Approach
Peer reviewed, Journal article
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OriginalversjonIFAC-PapersOnLine. 2020, 53 (2), 10084-10089. https://doi.org/10.1016/j.ifacol.2020.12.2731
In this paper, physiological signals were measured from five participants, each participating in two sessions of experiment with identical experimental procedure. A simulation platform, AutoCAMS (Automation-enhanced Cabin Air Management System), was used to simulate a complex task environment of human-machine shared process control. Fuzzy models were constructed to quantitatively predict the human operator performance based on three EEG input features. The incremental-PID-controlled particle swarm optimization (IPID-PSO) algorithm was utilized to optimize the parameters of fuzzy models. The IPID-PSO algorithm incorporated incremental-PID-controlled search strategy to speed up the convergence of standard PSO algorithm. The operator performance modeling results are given to show the effectiveness of the IPID-PSO-tuned fuzzy modeling approach proposed to momentary operator performance assessment problem under consideration.