DocumentCode :
2295454
Title :
Neural adaptive back stepping flight controller for a ducted fan UAV
Author :
Aruneshwaran, R. ; Wang Jianliang ; Suresh, Smitha ; Venugopalan, T.K.
Author_Institution :
Sch. of Electr. & Electron. Eng., NTU, Singapore, Singapore
fYear :
2012
fDate :
6-8 July 2012
Firstpage :
2370
Lastpage :
2375
Abstract :
In this paper, we present a neural adaptive back-stepping flight controller for a ducted fan UAV whose dynamics is characterized by uncertainties and highly coupled nonlinearities. The proposed neural adaptive back-stepping controller can handle unknown nonlinearities, unmodeled dynamics and external wind disturbances. A single layer radial basis function network is used to approximate the virtual control law derived using back stepping approach, which provides necessary stability and tracking performances. The neural controller parameters are adapted online using Lyapunov based update laws. The proposed controller is evaluated using nonlinear desktop simulation model of a typical ducted fan UAV performing bop-up maneuver. Three neural adaptive controllers are implemented to handle attitude command altitude hold system, one in each body axis. A separate neural controller is implemented to track the height command for autonomous takeoff and landing. The results indicate that the proposed controller can stabilize the ducted fan UAV and provide necessary tracking performance.
Keywords :
Lyapunov methods; adaptive control; aircraft landing guidance; approximation theory; attitude control; autonomous aerial vehicles; control nonlinearities; neurocontrollers; radial basis function networks; stability; Lyapunov based update law; attitude command altitude hold system; autonomous landing; autonomous takeoff; bop-up maneuver; ducted fan UAV; flight controller; height command tracking; neural adaptive back stepping controller; nonlinear desktop simulation model; nonlinearity handling; radial basis function network; stability; tracking performance; unmodeled dynamics; virtual control law approximation; wind disturbance; Aerodynamics; Asymptotic stability; Force; Mathematical model; Neural networks; Vectors; Vehicle dynamics; Adaptive Back stepping; Ducted fan; Lyapunov stability; Radial basis function neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation (WCICA), 2012 10th World Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-1397-1
Type :
conf
DOI :
10.1109/WCICA.2012.6358270
Filename :
6358270
Link To Document :
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