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Publisher DOI: 10.1002/pamm.202000036
Title: Nonlinear system identification of a furuta pendulum using machine learning techniques
Language: English
Authors: Rückwald, Tobias  
Drücker, Svenja  
Dücker, Daniel-André  
Seifried, Robert  
Issue Date: 25-Jan-2021
Publisher: Wiley-VCH
Source: Proceedings in applied mathematics and mechanics 20 (1) : e202000036 (2021)
Abstract (english): 
Usually, dynamical systems can be described by differential equations. An accurate model is essential when designing and optimizing a controller. However, not every system can be modeled easily by physical models due to highly nonlinear behavior, such as friction or backlash. Then, a data based approach, such as machine learning, might be helpful. The focus in this work is set on modeling dynamical systems using neural networks and deep learning, which are growing subjects in research and industry to identify nonlinear dynamics.
Conference: 91st Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM 2021) 
DOI: 10.15480/882.3903
ISSN: 1617-7061
Journal: Proceedings in applied mathematics and mechanics 
Institute: Mechanik und Meerestechnik M-13 
Document Type: Chapter/Article (Proceedings)
Project: Projekt DEAL 
License: CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives) CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
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