Title:
Adaptive Neural Network Flight Control Using both Current and Recorded Data
Adaptive Neural Network Flight Control Using both Current and Recorded Data
Author(s)
Chowdhary, Girish
Johnson, Eric N.
Johnson, Eric N.
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Abstract
Modern aerospace vehicles are expected to perform beyond their conventional flight
envelopes and exhibit the robustness and adaptability to operate in uncertain environments.
Augmenting proven lower level control algorithms with adaptive elements that exhibit long
term learning could help in achieving better adaptation performance while performing
aggressive maneuvers. The current adaptive methodologies which use Neural Network based
control methods use only the instantaneous states to tune the adaptive gains. This results in a
rank one limitation on the adaptive law. In this paper we propose a novel approach to
adaptive control, which uses the current or the online information as well as stored or
background information for adaptation. We show that using a combined online and
background learning approach it is possible to overcome the rank one limitation on the
adaptive law resulting in faster adaptation to the unknown dynamics. Furthermore, we show
that using combined online and background learning methods it is possible to guarantee long
term learning in the adaptive flight controller, which enhances performance of the controller
when it encounters a maneuver that has been performed in the past. We use Lyapunov
based methods for showing boundedness of all signals for a proposed method. The
performance of the proposed method is evaluated in the high fidelity simulation
environment for the GTMAX UAS maintained by the Georgia Tech UAV lab. The
simulation results show that the proposed method exhibits long term learning and faster
adaptation leading to better performance of the UAS flight controller.
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Date Issued
2007-08
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Text
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Proceedings