Individual Differences in Manual Control Cybernetics

Predicting Individual Cybernetic Parameters Using the Human Controller Cost Function

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Understanding individual differences in manual control cybernetics plays a principal role in personalizing human-machine systems. While much of the work in cybernetics utilizes models for the average controller, individualized models have also been explored, albeit constrained by data availability and the effectiveness of identification techniques. It has previously been theorized that human controller behavior may be defined by a cost function that the controller minimizes for a given control task. This cost function comprises weighted performance and effort terms, and the weightings have been connected to cybernetic models before, but not on an individualized basis. A proposed connection is through the equalization component of the cybernetic model, which has also been outlined as an indicator of individual differences. This claim was reinforced by the analysis of previously collected experiment data in this paper: Across three independent experiments, the equalization parameters, on average, showed 33.84% higher variation than the others and a greater impact on the cost function. Thus, this paper presents a human-controller-cost-function-centered approach to predict and generate individualized sets of cybernetic parameters. Individual cost function weightings were determined using the identified equalization parameters from earlier human-in-the-loop experiments. This process exhibited clear groupings in participants, as clusters of participants with similar equalization were not identified adequately. Individual cost function weightings were then employed to predict full sets of cybernetic parameters. The results indicated that the utilized cost function is incapable of reflecting the physical limitations of the human controller, as these parameters (time delay, neuromuscular natural frequency, and neuromuscular damping coefficient) showed 56.06%, -94.43%, and -170.34% deviation compared to the experimental values. Hence, a hybrid cybernetic data augmentation method was devised, yielding signal values within 15% of experimental data.