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Adaptive Control Using Neural Network Augmentation for a Modified F-15 AircraftDescription 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.
Document ID
20060023991
Acquisition Source
Armstrong Flight Research Center
Document Type
Conference Paper
Authors
Burken, John J. (NASA Dryden Flight Research Center Edwards, CA, United States)
Williams-Hayes, Peggy (NASA Dryden Flight Research Center Edwards, CA, United States)
Karneshige, J. T. (NASA Ames Research Center Moffett Field, CA, United States)
Stachowiak, Susan J. (NASA Johnson Space Center Houston, TX, United States)
Date Acquired
September 7, 2013
Publication Date
June 1, 2006
Subject Category
Aircraft Stability And Control
Meeting Information
Meeting: 14th Mediterranean Conference on Control and Automation
Location: Ancona
Country: Italy
Start Date: June 28, 2006
End Date: June 30, 2006
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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