2005 Special Issue: The control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle using a CMAC neural network

Authors:
Frederick G. Harmon;Andrew A. Frank;Sanjay S. Joshi
Affiliations:
Department of Mechanical and Aeronautical Engineering, University of California-Davis, Davis, CA 95616, USA;Department of Mechanical and Aeronautical Engineering, University of California-Davis, Davis, CA 95616, USA;Department of Mechanical and Aeronautical Engineering, University of California-Davis, Davis, CA 95616, USA
Venue:
Neural Networks - 2005 Special issue: IJCNN 2005
Year:
2005

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Neural network control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle

Neural network control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle

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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.