UKF-based Identification of Time-Varying Manual Control Behaviour
conference paper
This paper describes a novel method for time-varying identification of Human Controller (HC) manual control parameters (called UKF-FPV), based on a steady-state (constant state covariance) Unscented Kalman Filter (UKF). This approach requires no a priori assumptions on the shape of HO parameter variations, which is expected to be an advantage over other state-of-the-art methods, such as the recently proposed MLE-APV approach, for which a sigmoid-shaped parameter variation is assumed. For a scenario where an HO performs a single-loop compensatory tracking task with time-varying controlled system dynamics, both identification methods are compared using Monte Carlo simulations and human-in-the-loop experiment data. Despite some lag in the HO parameter traces of UKF-FPV, the identification results and the HC model quality-of-fit obtained with both methods were found to match well for both the simulation and experiment data. For the experiment data, UKF-FPV even revealed clear “local” changes in HC parameters not captured by theMLE-APV approach, which confirms that HCs adapt unpredictably even in time-invariant conditions. Overall, the results thus show that an identification method that requires no a priori assumptions on HC parameter variations is of critical importance for a complete analysis of time-varying HC behaviour.
TNO Identifier
867695
Source title
2019 IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems
Files
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