Estimation of Time-Varying Human Manual Control Behaviour in Preview Tracking Tasks Using a Dual Extended Kalman Filter
C. Vertregt (TU Delft - Aerospace Engineering)
DM Pool – Mentor (TU Delft - Control & Simulation)
M. Mulder – Graduation committee member (TU Delft - Control & Simulation)
Kasper El – Graduation committee member (TU Delft - Control & Simulation)
Mihaela Mitici – Graduation committee member (TU Delft - Air Transport & Operations)
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Abstract
This research applied a Dual Extended Kalman Filter (DEKF) to time-varying human operator (HO) parameter estimation in preview tracking tasks. The preview time parameter was of particular interest, as the amount of preview can be highly variable in practical situations. The filter was centered around a linear cybernetic HO model with six identifiable parameters for single-integrator and eight for double-integrator control tasks. The DEKF was applied to a range of time-varying simulations, both remnant-free as well as with realistic remnant based on an experimentally identified first-order model. In addition, the tests were validated using existing experimental tracking data. By keeping the HO physical limitation parameters constant (i.e. neuromuscular parameters ω_nms and ζ_nms and time delay τ_v), good estimation results were obtained, particularly for tracking of SI systems. General guidelines for the sensitive tuning process of the filter are proposed. The presence of coloured remnant noise in double-integrator tracking was found to greatly affect the quality of the estimates. Future versions of the filter should explicitly include models of remnant noise, to reduce its sensitivity and ease the tuning process. The obtained algorithm is applicable to single sets of measurement data without a priori assumptions on time-variance or the need for averaging. With some additional development, this makes the DEKF a suitable candidate for practical applications, such as driver monitoring and advanced driver assistance systems in the automotive industry.