Time-Varying Identification of Human Look-Ahead Times in Preview Tracking Tasks

Time-Varying Identification of Human Look-Ahead Times in Preview Tracking Tasks

Piera, Sybe (TU Delft Aerospace Engineering)

Pool, D.M. (mentor)
Mulder, Max (graduation committee)
van Paassen, M.M. (graduation committee)
de Winter, J.C.F. (graduation committee)
van der El, Kasper (mentor)

Delft University of Technology

Aerospace Engineering | Control & Simulation

2022-09-22

Future human-machine control tasks with preview (e.g., car driving) are expected to include automation for safety, but keep operators in charge for liability. Such shared control applications require time-varying human identification because the control feedback should be compatible with the operator's variable behavior. A promising time-domain identification algorithm is the Dual Extended Kalman Filter (DEKF), estimating human operator parameters from Van der El's preview model. In this article, the DEKF's time-varying identification performance is studied with realistic simulations, followed by human operator experiments in a fixed-base simulator. The investigation focuses on look-ahead time, indicating how much future information the operator uses for control. Compared to other parameters, look-ahead time is adapted most considerably with preview. The results suggest that this parameter should be initialized in a 0.25 s proximity of its actual value to make the DEKF converge within 30 s. Although only estimating look-ahead time while fixing the other parameters, the DEKF is capable of identifying time variations in preview. Based on the sigmoid results, the estimation bias increases linearly to 0.35 s at the largest 0.75 s steps in look-ahead time. For sine variations, the DEKF estimations are in phase with the look-ahead time until 0.03 rad/s. Between 0.03 rad/s and 0.4 rad/s the DEKF behaves as a lag function, and for higher frequencies the estimation response is decayed. For the first time, it is quantified how well the DEKF can identify variations in look-ahead time during preview tracking tasks. With further research, the DEKF might become capable of real-time identification, bringing the cybernetics community one step closer to intuitive shared control applications.

Manual Control
Cybernetics
Preview Display
Time-Varying Identification
Dual Extended Kalman Filter (DEKF)
Human-Machine Interaction (HMI) Experiments

http://resolver.tudelft.nl/uuid:7cd1fbba-0a3b-4692-95d8-6a0e47fc6aa9

2024-09-22

Student theses

master thesis

© 2022 Sybe Piera