Abstract
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 HC parameter variations, which is a potential advantage over 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 HC 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 HC 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 the MLE-APV approach, which confirms that HCs adapt unpredictably even in what are considered time-invariant conditions. Overall, the results 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.
Original language | English |
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Pages (from-to) | 109-114 |
Number of pages | 6 |
Journal | IFAC-PapersOnline |
Volume | 52 |
Issue number | 19 |
DOIs | |
Publication status | Published - 2019 |
Event | 14th IFAC Symposium on Analysis, Design, and Evaluation of Human Machine Systems, HMS 2019 - Tallinn, Estonia Duration: 16 Sept 2019 → 19 Sept 2019 |
Keywords
- Cybernetics
- manual control
- time-varying identification
- Unscented Kalman Filter