NDLI: Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

Content Provider	IEEE Xplore Digital Library
Author	Burken, J.J. Williams-Hayes, P. Kaneshige, J.T. Stachowiak, S.J.
Copyright Year	2006
Description	Author affiliation: NASA Dryden Flight Res. Center, Edwards, CA (Burken, J.J.; Williams-Hayes, P.)
Abstract	Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction
Starting Page	1
Ending Page	6
File Size	1177741
Page Count	6
File Format	PDF
ISBN	0978672011
DOI	10.1109/MED.2006.328857
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-28
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	MEDITERRANEAN CONTROL ASSOCIATION
Subject Keyword	Programmable control Neural networks NASA Aerodynamics Control systems Error correction Adaptive control Aircraft Vehicle dynamics Aerospace control
Content Type	Text
Resource Type	Article

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