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On data-driven nonlinear uncertainty modeling: Methods and application for control-oriented surface condition prediction in hard turning

Wittich, Felix
Kistner, Lars
Kroll, Andreas
Schott, Christopher
Niendorf, Thomas

In this article, two data-driven modeling approaches are investigated, which allow an explicit modeling of uncertainty. For this purpose, parametric Takagi-Sugeno multi-models with bounded-error parameter estimation and nonparametric Gaussian process regression are applied and compared. These models can for instance be used for robust model-based control design. As an application, the prediction of residual stresses during hard turning depending on the machining parameters and the initial hardness is considered.

Sponsor
The scientific work has been supported by the DFG within the research priority program SPP 2086 (Grant Number: KR 3795/8-1; NI 1327/22-1; ZI 1296/2-1).
Citation
In: tm - Technisches Messen Band 87 / Heft 11 (2020-10-16) , S. 732-741; eissn:2196-7113
Collections
@article{doi:10.17170/kobra-202307198403,
  author    ={Wittich, Felix and Kistner, Lars and Kroll, Andreas and Schott, Christopher and Niendorf, Thomas},
  title    ={On data-driven nonlinear uncertainty modeling: Methods and application for control-oriented surface condition prediction in hard turning},
  keywords ={004 and 620 and Unsicherheit and Modell and Takagi-Sugeno-Regler and Gauß-Prozess and Regressionsmodell and Hartdrehen},
  copyright  ={https://rightsstatements.org/page/InC/1.0/},
  language ={en},
  journal  ={tm - Technisches Messen},
  year   ={2020-10-16}
}