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.
Recommended Citation
G. de La Torre et al., "Neuropredictive Control and Trajectory Generation for Slung Load Systems," AIAA Infotech at Aerospace (I at A) Conference, American Institute of Aeronautics and Astronautics, Jan 2013.
The definitive version is available at https://doi.org/10.2514/6.2013-5044
Department(s)
Mechanical and Aerospace Engineering
Publication Status
Full Access
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 American Institute of Aeronautics and Astronautics, All rights reserved.
Publication Date
01 Jan 2013
