Eisentraudt M, Leyendecker S (2019)
Publication Type: Journal article, Original article
Publication year: 2019
Book Volume: 126
Pages Range: 590-608
DOI: 10.1016/j.ymssp.2019.02.036
In this paper, fuzzy uncertainty in forward dynamics simulation is considered. The output of the dynamical system – a fuzzy function – is determined on the basis of α" role="presentation" style="display: inline-block; line-height: normal; font-size: 16.2px; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">α-discretisation together with α" role="presentation" style="display: inline-block; line-height: normal; font-size: 16.2px; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">α-level optimisation. A new method for an efficient realisation of α" role="presentation" style="display: inline-block; line-height: normal; font-size: 16.2px; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">α-level optimisation is developed, which is applicable to arbitrary time integration schemes. The method, called ‘Graph Follower’, is based on combinations of local optimisations and time integration steps. Different formulations of the underlying α" role="presentation" style="display: inline-block; line-height: normal; font-size: 16.2px; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">α-level optimisation problem are derived and examined. In particular, an approximative description of the output as a function of the parameters is introduced, which enables a significant reduction of the numerical complexity of the developed method.
APA:
Eisentraudt, M., & Leyendecker, S. (2019). Fuzzy uncertainty in forward dynamics simulation. Mechanical Systems and Signal Processing, 126, 590-608. https://doi.org/10.1016/j.ymssp.2019.02.036
MLA:
Eisentraudt, Markus, and Sigrid Leyendecker. "Fuzzy uncertainty in forward dynamics simulation." Mechanical Systems and Signal Processing 126 (2019): 590-608.
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