Neuropredictive control and trajectory generation for slung load systems

Gerardo de La Torre, Eric N. Johnson, Tansel Yucelen

Research output: Contribution to conferencePaper

1 Citation (Scopus)

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 languageEnglish (US)
StatePublished - Sep 16 2013
EventAIAA Infotech at Aerospace (I at A) Conference - Boston, MA, United States
Duration: Aug 19 2013Aug 22 2013

Other

OtherAIAA Infotech at Aerospace (I at A) Conference
CountryUnited States
CityBoston, MA
Period8/19/138/22/13

Fingerprint

Slings
Trajectories
Neural networks
Model predictive control
Uncertainty

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

Cite this

de La Torre, G., Johnson, E. N., & Yucelen, T. (2013). Neuropredictive control and trajectory generation for slung load systems. Paper presented at AIAA Infotech at Aerospace (I at A) Conference, Boston, MA, United States.
de La Torre, Gerardo ; Johnson, Eric N. ; Yucelen, Tansel. / Neuropredictive control and trajectory generation for slung load systems. Paper presented at AIAA Infotech at Aerospace (I at A) Conference, Boston, MA, United States.
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de La Torre, G, Johnson, EN & Yucelen, T 2013, 'Neuropredictive control and trajectory generation for slung load systems', Paper presented at AIAA Infotech at Aerospace (I at A) Conference, Boston, MA, United States, 8/19/13 - 8/22/13.

Neuropredictive control and trajectory generation for slung load systems. / de La Torre, Gerardo; Johnson, Eric N.; Yucelen, Tansel.

2013. Paper presented at AIAA Infotech at Aerospace (I at A) Conference, Boston, MA, United States.

Research output: Contribution to conferencePaper

TY - CONF

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AU - de La Torre, Gerardo

AU - Johnson, Eric N.

AU - Yucelen, Tansel

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N2 - 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.

AB - 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.

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de La Torre G, Johnson EN, Yucelen T. Neuropredictive control and trajectory generation for slung load systems. 2013. Paper presented at AIAA Infotech at Aerospace (I at A) Conference, Boston, MA, United States.