Engineering and Computer Science Faculty Publications
The Control of a Parallel Hybrid-Electric Propulsion System for a Small Unmanned Aerial Vehicle Using a CMAC Neural Network
Document Type
Article
Publication Date
8-2005
Journal Title
Neural Networks
Volume
18
Issue
5-6
First Page
772
Last Page
780
DOI
10.1016/j.neunet.2005.06.030
Abstract
A Simulink model, a propulsion energy optimization algorithm, and a CMAC controller were developed for a small parallel hybrid-electric unmanned aerial vehicle (UAV). The hybrid-electric UAV is intended for military, homeland security, and disaster-monitoring missions involving intelligence, surveillance, and reconnaissance (ISR). The Simulink model is a forward-facing simulation program used to test different control strategies. The flexible energy optimization algorithm for the propulsion system allows relative importance to be assigned between the use of gasoline, electricity, and recharging. A cerebellar model arithmetic computer (CMAC) neural network approximates the energy optimization results and is used to control the parallel hybrid-electric propulsion system. The hybrid-electric UAV with the CMAC controller uses 67.3% less energy than a two-stroke gasoline-powered UAV during a 1-h ISR mission and 37.8% less energy during a longer 3-h ISR mission.
Keywords
UAV, unmanned aerial vehicle, propulsion
Recommended Citation
Harmon, Frederick G.; Frank, A. A.; and Joshi, S. S., "The Control of a Parallel Hybrid-Electric Propulsion System for a Small Unmanned Aerial Vehicle Using a CMAC Neural Network" (2005). Engineering and Computer Science Faculty Publications. 180.
https://digitalcommons.cedarville.edu/engineering_and_computer_science_publications/180
