Nonlinear System Identification of a Furuta Pendulum Using Machine Learning Techniques

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 appr...

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Bibliographic Details
Published in:Proceedings in applied mathematics and mechanics 2021-01, Vol.20 (1), p.n/a
Main Authors: Rückwald, Tobias, Drücker, Svenja, Dücker, Daniel-André, Seifried, Robert
Format: Article
Language:English
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Summary: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.
ISSN:1617-7061
1617-7061
DOI:10.1002/pamm.202000036