Control on a 2-D Wing Flutter Using an AdaptiveNonlinear Neural Controller

An adaptive nonlinear neural controller to reduce the nonlinear flutter in 2-D wing is proposed in the paper. The nonlinearities in the system come from the quasi steady aerodynamic model and torsional spring in pitch direction. Time domain simulations are used to examine the dynamic aero elastic in...

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Autores principales:	Hayder S. Abd Al-Amir, Mohammed I. Mohsin, Mauwafak A. Tawfik
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2011
Materias:	Adaptive Nonlinear Control Flutter Nonlinear system Neural Network. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/875285c2fe53461caacd787109b61006
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spelling	oai:doaj.org-article:875285c2fe53461caacd787109b610062021-12-02T05:28:19ZControl on a 2-D Wing Flutter Using an AdaptiveNonlinear Neural Controller1818-1171https://doaj.org/article/875285c2fe53461caacd787109b610062011-01-01T00:00:00Zhttp://www.iasj.net/iasj?func=fulltext&aId=2212https://doaj.org/toc/1818-1171An adaptive nonlinear neural controller to reduce the nonlinear flutter in 2-D wing is proposed in the paper. The nonlinearities in the system come from the quasi steady aerodynamic model and torsional spring in pitch direction. Time domain simulations are used to examine the dynamic aero elastic instabilities of the system (e.g. the onset of flutter and limit cycle oscillation, LCO). The structure of the controller consists of two models :the modified Elman neural network (MENN) and the feed forward multi-layer Perceptron (MLP). The MENN model is trained with off-line and on-line stages to guarantee that the outputs of the model accurately represent the plunge and pitch motion of the wing and this neural model acts as the identifier. The feed forward neural controller is trained off-line and adaptive weights are implemented on-line to find the flap angles, which controls the plunge and pitch motion of the wing. The general back propagation algorithm is used to learn the feed forward neural controller and the neural identifier. The simulation results show the effectiveness of the proposed control algorithm; this is demonstrated by the minimized tracking error to zero approximation with very acceptable settling time even with the existence of bounded external disturbances.Hayder S. Abd Al-AmirMohammed I. MohsinMauwafak A. TawfikAl-Khwarizmi College of Engineering – University of BaghdadarticleAdaptive Nonlinear ControlFlutterNonlinear systemNeural Network.Chemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 7, Iss 4, Pp 27-40 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Adaptive Nonlinear Control Flutter Nonlinear system Neural Network. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Adaptive Nonlinear Control Flutter Nonlinear system Neural Network. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Hayder S. Abd Al-Amir Mohammed I. Mohsin Mauwafak A. Tawfik Control on a 2-D Wing Flutter Using an AdaptiveNonlinear Neural Controller
description	An adaptive nonlinear neural controller to reduce the nonlinear flutter in 2-D wing is proposed in the paper. The nonlinearities in the system come from the quasi steady aerodynamic model and torsional spring in pitch direction. Time domain simulations are used to examine the dynamic aero elastic instabilities of the system (e.g. the onset of flutter and limit cycle oscillation, LCO). The structure of the controller consists of two models :the modified Elman neural network (MENN) and the feed forward multi-layer Perceptron (MLP). The MENN model is trained with off-line and on-line stages to guarantee that the outputs of the model accurately represent the plunge and pitch motion of the wing and this neural model acts as the identifier. The feed forward neural controller is trained off-line and adaptive weights are implemented on-line to find the flap angles, which controls the plunge and pitch motion of the wing. The general back propagation algorithm is used to learn the feed forward neural controller and the neural identifier. The simulation results show the effectiveness of the proposed control algorithm; this is demonstrated by the minimized tracking error to zero approximation with very acceptable settling time even with the existence of bounded external disturbances.
format	article
author	Hayder S. Abd Al-Amir Mohammed I. Mohsin Mauwafak A. Tawfik
author_facet	Hayder S. Abd Al-Amir Mohammed I. Mohsin Mauwafak A. Tawfik
author_sort	Hayder S. Abd Al-Amir
title	Control on a 2-D Wing Flutter Using an AdaptiveNonlinear Neural Controller
title_short	Control on a 2-D Wing Flutter Using an AdaptiveNonlinear Neural Controller
title_full	Control on a 2-D Wing Flutter Using an AdaptiveNonlinear Neural Controller
title_fullStr	Control on a 2-D Wing Flutter Using an AdaptiveNonlinear Neural Controller
title_full_unstemmed	Control on a 2-D Wing Flutter Using an AdaptiveNonlinear Neural Controller
title_sort	control on a 2-d wing flutter using an adaptivenonlinear neural controller
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2011
url	https://doaj.org/article/875285c2fe53461caacd787109b61006
work_keys_str_mv	AT haydersabdalamir controlona2dwingflutterusinganadaptivenonlinearneuralcontroller AT mohammedimohsin controlona2dwingflutterusinganadaptivenonlinearneuralcontroller AT mauwafakatawfik controlona2dwingflutterusinganadaptivenonlinearneuralcontroller
_version_	1718400361755049984

Control on a 2-D Wing Flutter Using an AdaptiveNonlinear Neural Controller

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