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Published in:
2020 | OriginalPaper | Chapter
Possibilistic Investigation of Mechanical Control Systems Under Uncertainty
Authors : Andreas Hofmann, Michael Hanss, Peter Eberhard
Published in: Multibody Dynamics 2019
Publisher: Springer International Publishing
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Abstract
Uncertainty in linear control systems is often modeled by real- or complex-valued perturbations. The key idea of robust control techniques is to check whether the design specifications are satisfied even for the worst-case uncertainty. A quite intuitive and apparently natural way to model uncertainty is using fuzzy sets and arithmetic. The resulting uncertain system models do not only consider worst-case scenarios, but also the shape of uncertainty modeled by imprecise quantities. Possibilistic measures can then be employed to investigate the satisfaction of stability and performance objectives and to draw conclusions about the robustness of the resulting controller. The gradualness in the quantification of uncertainty allows for more comprehensive statements than just worst-case scenarios.