Abstract
In manual control tasks, preview of the target trajectory ahead is often limited by poor lighting, objects, or display edges. This paper investigates the effects of limited preview, or preview time, in manual tracking tasks with single- and double-integrator controlled element dynamics. A quasi-linear human controller model is used to predict the human behavior adaptations offline, by finding the model parameters that yield optimal performance at each preview time. These predictions are then verified by fitting the same model to measurements from a human-in-the-loop experiment, where subjects performed a tracking task with eight different preview time settings between 0 and 2 s. Results show that the tracking performance improves and the model’s “look-ahead” time parameters increase with increasing preview time. Beyond a certain preview time, approximately 0.6 s and 1.15 s in single- and double-integrator tasks, respectively, additional preview evokes no further adaptations. The offline model predictions closely match the experimental results, which thereby promises to facilitate similar quantitative insights in other tasks with restricted preview.
| Original language | English |
|---|---|
| Pages (from-to) | 486 - 495 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Human-Machine Systems |
| Volume | 48 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2018 |
Keywords
- Adaptation models
- Data models
- Manual control
- Manuals
- modeling
- parameter estimation
- Predictive models
- preview time
- system identification
- Target tracking
- Task analysis
- Trajectory