Name
Abstract | ||
|---|---|---|
Modeling of dynamical systems is essential in many areas of engineering, such as product development and condition monitoring. Currently, the two main approaches in modeling of dynamical systems are the physical and the data-driven one. Both approaches are sufficient for a wide range of applications but suffer from various disadvantages, e.g., a reduced accuracy due to the limitations of the physical model or due to missing data. In this work, a methodology for modeling dynamical systems is introduced, which expands the area of application by combining the advantages of both approaches while weakening the respective disadvantages. The objective is to obtain increased accuracy with reduced complexity. Two models are used, a physical model predicts the system behavior in a simplified manner, while the data-driven model accounts for the discrepancy between reality and the simplified model. This hybrid approach is validated experimentally on a double pendulum. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-95470-3_8 | MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE (LOD 2021), PT II |
Keywords | DocType | Volume |
Hybrid modeling, Gray-box modeling, Double pendulum, Modeling of dynamical systems with friction | Conference | 13164 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meike Wohlleben | 1 | 0 | 0.34 |
| Amelie Bender | 2 | 0 | 0.34 |
| Peitz Sebastian | 3 | 3 | 1.15 |
| Walter Sextro | 4 | 0 | 0.34 |