Abstract
As hydraulic drive technology advances, there is an increasing demand for accurate modelling of hydraulic drive systems for design analysis and full life cycle management. In this paper, an AMESim simulation model is established for an aircraft bay door hydraulic drive system test bed. Since there is a large error between the output of the simulation model and the actual, an error compensation model is established for the AMESim output and the physical output. This paper proposes a combined GRU-Attention-ELM model for the establishment of the error compensation model, and compares it with other commonly used machine learning models, the fitting effect is better than other models, and the accuracy of the compensated simulation output is greatly improved.
| Original language | English |
|---|---|
| Title of host publication | CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024 |
| Publisher | Institution of Engineering and Technology |
| Pages | 842-846 |
| Number of pages | 5 |
| Volume | 2024 |
| Edition | 13 |
| ISBN (Electronic) | 9781837242108 |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024 - Xi�an, China Duration: 16 Aug 2024 → 19 Aug 2024 |
Conference
| Conference | 2024 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024 |
|---|---|
| Country/Territory | China |
| City | Xi�an |
| Period | 16/08/24 → 19/08/24 |
Keywords
- ERROR PREDICTION
- HYDRAULIC SYSTEM
- NEURAL NETWORK MODELING
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