English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Manual Control with Pursuit Displays New Insights, New Models, New Issues

MPS-Authors
/persons/resource/persons84423

Drop,  FM
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Mulder, M., Pool, D., van der El, K., Drop, F., & van Paassen, R. (2019). Manual Control with Pursuit Displays New Insights, New Models, New Issues. In 14th IFAC Symposium on Analysis Design and Evaluation of Human Machine Systems (HMS 2019) (pp. 139-144).


Cite as: http://hdl.handle.net/21.11116/0000-0004-71B0-5
Abstract
Mathematical control models are widely used in tuning manual control systems and understanding human performance. The most common model, the crossover model, is severely limited however in describing realistic human control behaviour in relevant control tasks as it is only valid for tracking with a compensatory display. This paper first discusses the state-of- the-art in modelling human control in tracking with pursuit displays. It is shown that, although both tasks seem very similar, the separate presentation of target and system output signals allows operators to adopt a huge variety in control strategies, which makes the development of a universal model for pursuit control a challenge. Two recent models are then described which can act as precursors to this universal model. Third, system identification choices and issues are discussed for pursuit tracking tasks. Finally it is argued that it is inevitable that time-varying rather than time-invariant methods are needed to properly describe human behaviour in the pursuit tracking task, as skilled operators will learn to characterize the probabilistic nature of the task which cannot be captured in a single, linear, time-invariant model.