Please use this identifier to cite or link to this item:
https://doi.org/10.15480/882.3903
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) | URI: | http://hdl.handle.net/11420/10884 | 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: | ![]() |
Appears in Collections: | Publications with fulltext |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
pamm.202000036.pdf | Verlagsversion | 2,02 MB | Adobe PDF | View/Open![]() |
Page view(s)
181
Last Week
2
2
Last month
checked on Mar 31, 2023
Download(s)
135
checked on Mar 31, 2023
Google ScholarTM
Check
Note about this record
Cite this record
Export
This item is licensed under a Creative Commons License