Abstract
Human modelling approaches are typically limited to feedback-only, compensatory tracking tasks. Advances in system identification techniques allow us to consider more realistic tasks that involve feedforward and even precognitive control. In this paper we study the human development of a feedforward control response while learning to accurately follow a ramp-shaped target signal in the presence of a disturbance acting on the controlled element. An experiment was conducted in which two groups of eight subjects each tracked ramps of different steepnesses in a random or ordered fashion. In addition, ordered runs were followed by a 'surprise' run with a random ramp steepness. Results show that operators learn rapidly, continue to learn during the entire experiment, and can adapt very quickly to surprise situations. Experiments involving learning operators are challenging, as it is difficult to balance-out all experimental conditions and control for inevitable differences between (groups of) subjects.
Original language | English |
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Pages (from-to) | 235-240 |
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
- learning
- manual control
- modeling
- skill
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Dive into the research topics of 'Analysis of Human Skill Development in Manual Ramp-Tracking Tasks'. Together they form a unique fingerprint.Prizes
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IFAC HMS 2019 Best Paper Award
Willems, Mareijn (Recipient), Pool, D.M. (Recipient), van der El, K. (Recipient), Damveld, Herman (Recipient), van Paassen, M.M. (Recipient) & Mulder, M. (Recipient), 19 Sept 2019
Prize: Prize (including medals and awards)
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