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Aufsatz
On data-driven nonlinear uncertainty modeling: Methods and application for control-oriented surface condition prediction in hard turning
(2020-10-16)
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.
Zeitschrift
Jahresbericht 2016
(Universität Kassel, Fachbereich Wirtschaftswissenschaften, 2017)
Zeitschrift
Jahresbericht 2017
(Universität Kassel, Fachbereich Wirtschaftswissenschaften, 2018)
Zeitschrift
Jahresbericht 2012
(Universität Kassel, Fachbereich Wirtschaftswissenschaften, 2013)
Zeitschrift
Jahresbericht 2019
(Universität Kassel, Fachbereich Wirtschaftswissenschaften, 2020)
Zeitschrift
Jahresbericht 2009/2010
(Universität Kassel, Fachbereich Wirtschaftswissenschaften, 2011)
Zeitschrift
Jahresbericht 2013/2014
(Universität Kassel, Fachbereich Wirtschaftswissenschaften, 2015)
Zeitschrift
Jahresbericht 2011
(Universität Kassel, Fachbereich Wirtschaftswissenschaften, 2012)
Zeitschrift
Jahresbericht 2018
(Universität Kassel, Fachbereich Wirtschaftswissenschaften, 2019)
Dissertation
Hybrid Branching-Time Logics
(2019)
We introduce and study an extension of the well-known and well-studied branching-time logics CTL, CTL+, FCTL+, CTL* and the modal μ-calculus with the so called "hybrid framework". This framework borrows ideas from first-order logic to enable more precise "structural" reasoning which is known to be impossible in the original logics. In particular, the extension with this framework enables these logics to uniquely name, reference and test for certain states, similarly to the concepts of variables and constants in ...