Abstract
This paper presents a neuropredictive trajectory generation architecture for slung load systems. The presented architecture integrates the real-time trajectory generation method with a system uncertainty identifying neural network. It is shown that the effect of system uncertainty on a model predictive control approach can be mitigated by the use of neural networks. A numerical example of an uncertain slung load system is shown to demonstrate the effectiveness of the presented framework.
| Original language | English (US) |
|---|---|
| State | Published - Sep 16 2013 |
| Event | AIAA Infotech at Aerospace (I at A) Conference - Boston, MA, United States Duration: Aug 19 2013 → Aug 22 2013 |
Other
| Other | AIAA Infotech at Aerospace (I at A) Conference |
|---|---|
| Country/Territory | United States |
| City | Boston, MA |
| Period | 8/19/13 → 8/22/13 |
All Science Journal Classification (ASJC) codes
- Aerospace Engineering
Fingerprint
Dive into the research topics of 'Neuropredictive control and trajectory generation for slung load systems'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver