Abstract
The introduction of ever-advancing automatic control systems is rapidly changing traditional manual control tasks such as piloting of aircraft and steering of cars. In order to predict how human controllers will interact with new technology, a thorough understanding of the human’s adaptive manual control capabilities and limitations is essential. This thesis investigates human manual control behavior in control tasks with preview, where information is available about the trajectory to follow in the future; an example is the road that is visible ahead while driving. Human control behavior is measured in tasks that range from basic display tracking (yellow grid on this cover) to realistic car curve driving. A unifying control theoretic model is developed, which captures the measured human control behavior in all preview tasks. The proposed theoretical advancements do not only improve our understanding of manual preview control, but also pave the way for an objective model-based approach to optimize the design of tomorrow’s intelligent automation technology.
| Original language | English |
|---|---|
| Qualification | Doctor of Philosophy |
| Awarding Institution |
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| Supervisors/Advisors |
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| Award date | 6 Dec 2018 |
| Print ISBNs | 978-94-6186-967-8 |
| DOIs | |
| Publication status | Published - 2018 |
Keywords
- Preview control
- Manual control
- Human behavior analysis
- Tracking tasks
- Driver steering
- System identification
- Parameter estimation
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Dive into the research topics of 'How humans use preview information in manual control'. Together they form a unique fingerprint.Prizes
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IEEE Thesis Grant Initiative PhD-HMS Grant
van der El, K. (Recipient), Pool, D.M. (Recipient), van Paassen, M.M. (Recipient) & Mulder, M. (Recipient), 22 Oct 2020
Prize: Prize (including medals and awards)