Title:
Flight Test Validation of a Neural Network based Long Term Learning Adaptive Flight Controller
Flight Test Validation of a Neural Network based Long Term Learning Adaptive Flight Controller
Author(s)
Chowdhary, Girish
Johnson, Eric N.
Johnson, Eric N.
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Abstract
The purpose of this paper is to present and analyze flight test results of a Long Term
Learning Adaptive Flight Controller implemented on a rotorcraft and a fixed wing
Unmanned Aerial Vehicle. The adaptive control architecture used is based on a proven
Model Reference Adaptive Control (MRAC) architecture employing a Neural Network as
the adaptive element. The method employed for training the Neural Network for these flight
tests is unique since it uses current (online) as well as stored (background) information
concurrently for adaptation. This ability allows the adaptive element to simulate long term
memory by retaining specifically stored input output data pairs and using them for
concurrent adaptation. Furthermore, the structure of the adaptive law ensures that
concurrent training on past data does not affect the responsiveness of the adaptive element
to current data. The results show that the concurrent use of current and background data
does not affect the practical stability properties of the MRAC control architecture. The
results also confirm expected improvements in performance.
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Date Issued
2009-08
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Proceedings