Control on a 2-D Wing Flutter Using an Adaptive Nonlinear 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 i...

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Autores principales:	Mauwafak A. Tawfik, Mohammed I. Mohsin, Hayder S. Abd Al-Amir
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2018
Materias:	Keywords: 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/dee9ba0949da4759963e23dd2e886bfb
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spelling	oai:doaj.org-article:dee9ba0949da4759963e23dd2e886bfb2021-12-02T06:16:27ZControl on a 2-D Wing Flutter Using an Adaptive Nonlinear Neural Controller1818-11712312-0789https://doaj.org/article/dee9ba0949da4759963e23dd2e886bfb2018-01-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/78https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 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. Mauwafak A. TawfikMohammed I. MohsinHayder S. Abd Al-AmirAl-Khwarizmi College of Engineering – University of BaghdadarticleKeywords: Adaptive Nonlinear Control, Flutter, Nonlinear system, Neural Network.Chemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 7, Iss 4 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Keywords: Adaptive Nonlinear Control, Flutter, Nonlinear system, Neural Network. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Keywords: Adaptive Nonlinear Control, Flutter, Nonlinear system, Neural Network. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Mauwafak A. Tawfik Mohammed I. Mohsin Hayder S. Abd Al-Amir Control on a 2-D Wing Flutter Using an Adaptive Nonlinear 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	Mauwafak A. Tawfik Mohammed I. Mohsin Hayder S. Abd Al-Amir
author_facet	Mauwafak A. Tawfik Mohammed I. Mohsin Hayder S. Abd Al-Amir
author_sort	Mauwafak A. Tawfik
title	Control on a 2-D Wing Flutter Using an Adaptive Nonlinear Neural Controller
title_short	Control on a 2-D Wing Flutter Using an Adaptive Nonlinear Neural Controller
title_full	Control on a 2-D Wing Flutter Using an Adaptive Nonlinear Neural Controller
title_fullStr	Control on a 2-D Wing Flutter Using an Adaptive Nonlinear Neural Controller
title_full_unstemmed	Control on a 2-D Wing Flutter Using an Adaptive Nonlinear Neural Controller
title_sort	control on a 2-d wing flutter using an adaptive nonlinear neural controller
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2018
url	https://doaj.org/article/dee9ba0949da4759963e23dd2e886bfb
work_keys_str_mv	AT mauwafakatawfik controlona2dwingflutterusinganadaptivenonlinearneuralcontroller AT mohammedimohsin controlona2dwingflutterusinganadaptivenonlinearneuralcontroller AT haydersabdalamir controlona2dwingflutterusinganadaptivenonlinearneuralcontroller
_version_	1718400022441099264

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

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