Probabilistic Perspective on Compensatory, Pursuit and Preview Manual Control

Journal Article (2022)
Author(s)

M Mulder (TU Delft - Control & Simulation)

D.M. Pool (TU Delft - Control & Simulation)

Kasper van der El (TU Delft - Control & Simulation)

MM van Paassen (TU Delft - Control & Simulation)

Research Group
Control & Simulation
Copyright
© 2022 Max Mulder, D.M. Pool, Kasper van der El, M.M. van Paassen
DOI related publication
https://doi.org/10.1016/j.ifacol.2022.10.248
More Info
expand_more
Publication Year
2022
Language
English
Copyright
© 2022 Max Mulder, D.M. Pool, Kasper van der El, M.M. van Paassen
Research Group
Control & Simulation
Issue number
29
Volume number
55
Pages (from-to)
154-159
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Mathematical human control models are widely used in tuning manual control systems and understanding human performance. Human behavior is commonly described using linear time-invariant models, averaging-out all non-linear and time-varying effects, which are gathered into the remnant. These models are limited in their capability to capture particular tracking strategies that an experienced subject may learn to use. In this paper, we consider manual control from a different perspective, namely through investigating the probability densities of the tracking error for different regions of the target signal amplitude. Results show that distinct strategies become apparent for compensatory, pursuit and preview tracking tasks. Effects of these strategies are often averaged-out by current models and can only be captured in situation-dependent models. Modeling this systematic human adaptation not captured in linear models could potentially lead to better model fits and explain/reduce part of the remnant.