Abstract

A reconfigurable flight control method is developed to be implemented on an Unmanned Aircraft (UA), a thirty percent scale model of the Cessna 150. This paper presents the details of the UA platform, system identification, reconfigurable controller design, development, and implementation on the UA to analyze the performance metrics. A Crossbow Inertial Measurement Unit provides the roll, pitch, and yaw accelerations and rates along with the roll and pitch. The 100-400 mini-air data boom from SpaceAge Control provides the airspeed, altitude, angle of attack, and the side slip angles. System identification is accomplished by commanding preprogrammed inputs to the control surfaces and correlating the corresponding variations at the outputs. A Single Network Adaptive Critic, which is a neural network-based optimal controller, is developed as part of a nonlinear flight control system. An online learning neural network is augmented to form an outer loop to reconfigure and supplement the optimal controller to guarantee a "practical stability" for the airplane. This paper also presents some simulations from the hardware-in-the-loop testing and concludes with an analysis of the flight performance metrics for the controller under investigation.

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

Control Equipment; Identification (Control Systems); Learning Systems; Neural Networks

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.


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