Abstract
The optimal control model for the human operator has emerged as one of the most promising models for the study of human performance in complex tasks. Previous applications of this model have used heuristic methods based on empirical data to establish numerical values for the model parameters. This was necessary because of the absence of any systematic identification method for the direct extraction of model parameters from experimental data. In this paper, the standard optimal control model is analyzed from the viewpoint of system identification. It is shown that the existing model structure is overparameterized and can be simplified by modifying some of the original assumptions. Identifiability of the resulting modified optimal control model is investigated. As a result, a systematic procedure for the identification of the modified optimal control model parameters from available data is developed. This procedure is validated by application to experimental data from simulation of a piloted tracking task. The paper concludes with recommendations for further simplification in the human operator model structure.
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