Frederick G. Harmon

Affiliations: 
Aeronautics & Astronautics (ENY) Air Force Institute of Technology 
Area:
Aerospace Engineering
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"Frederick Harmon"
Bio:

https://books.google.com/books?id=ZKQvAQAAIAAJ
http://handle.dtic.mil/100.2/ADA433531

Parents

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Andrew Alfonso Frank grad student 2005 UC Davis (E-Tree)
 (Neural network control of a parallel hybrid -electric propulsion system for a small unmanned aerial vehicle.)
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Publications

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Ross SM, Cobb RG, Baker WP, et al. (2015) Implementation lessons and pitfalls for real-time optimal control with stochastic systems Optimal Control Applications and Methods. 36: 198-217
Ausserer JK, Harmon FG. (2012) Integration, validation, and testing of a hybrid-electric propulsion system for a small remotely-piloted aircraft 10th Annual International Energy Conversion Engineering Conference, Iecec 2012
Greiser CM, Mengistu IH, Rotramel TA, et al. (2011) Testing of a parallel hybrid-electric propulsion system for use in a small remotely-piloted aircraft 9th Annual International Energy Conversion Engineering Conference, Iecec 2011
Hiserote RM, Harmon FG. (2010) Analysis of hybrid-electric propulsion system designs for small unmanned aircraft systems 8th Annual International Energy Conversion Engineering Conference
Hrad PM, Harmon FG. (2010) Conceptual design tool for Micro Air Vehicles with hybrid power systems 8th Annual International Energy Conversion Engineering Conference
Harmon FG, Frank AA, Chattot JJ. (2006) Conceptual design and simulation of a small hybrid-electric unmanned aerial vehicle Journal of Aircraft. 43: 1490-1498
Harmon FG, Frank AA, Joshi SS. (2005) The control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle using a CMAC neural network. Neural Networks : the Official Journal of the International Neural Network Society. 18: 772-80
Harmon FG, Frank AA, Joshi SS. (2005) Application of a CMAC neural network to the control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle Proceedings of the International Joint Conference On Neural Networks. 1: 355-360
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