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