Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft
This paper presents an adaptive neural control for the longitudinal dynamics of a morphing aircraft. Based on the functional decomposition, it is reasonable to decompose the longitudinal dynamics into velocity and altitude subsystems. As for the velocity subsystem, the adaptive control is proposed v...
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2015-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/787931 |
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doaj-7bcff911e7a74ab9b4d0bb63aaeb14d72020-11-24T23:59:33ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/787931787931Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing AircraftZhonghua Wu0Jingchao Lu1Jahanzeb Rajput2Jingping Shi3Wen Ma4School of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaScience and Technology on Aircraft Control Laboratory, AVIC Xi’an Flight Automatic Control Research Institute, Xi’an 710065, ChinaThis paper presents an adaptive neural control for the longitudinal dynamics of a morphing aircraft. Based on the functional decomposition, it is reasonable to decompose the longitudinal dynamics into velocity and altitude subsystems. As for the velocity subsystem, the adaptive control is proposed via dynamic inversion method using neural network. To deal with input constraints, the additional compensation system is employed to help engine recover from input saturation rapidly. The highlight is that high order integral chained differentiator is used to estimate the newly defined variables and an adaptive neural controller is designed for the altitude subsystem where only one neural network is employed to approximate the lumped uncertain nonlinearity. The altitude subsystem controller is considerably simpler than the ones based on backstepping. It is proved using Lyapunov stability theory that the proposed control law can ensure that all the tracking error converges to an arbitrarily small neighborhood around zero. Numerical simulation study demonstrates the effectiveness of the proposed strategy, during the morphing process, in spite of some uncertain system nonlinearity.http://dx.doi.org/10.1155/2015/787931 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhonghua Wu Jingchao Lu Jahanzeb Rajput Jingping Shi Wen Ma |
spellingShingle |
Zhonghua Wu Jingchao Lu Jahanzeb Rajput Jingping Shi Wen Ma Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft Mathematical Problems in Engineering |
author_facet |
Zhonghua Wu Jingchao Lu Jahanzeb Rajput Jingping Shi Wen Ma |
author_sort |
Zhonghua Wu |
title |
Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft |
title_short |
Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft |
title_full |
Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft |
title_fullStr |
Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft |
title_full_unstemmed |
Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft |
title_sort |
adaptive neural control based on high order integral chained differentiator for morphing aircraft |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
This paper presents an adaptive neural control for the longitudinal dynamics of a morphing aircraft. Based on the functional decomposition, it is reasonable to decompose the longitudinal dynamics into velocity and altitude subsystems. As for the velocity subsystem, the adaptive control is proposed via dynamic inversion method using neural network. To deal with input constraints, the additional compensation system is employed to help engine recover from input saturation rapidly. The highlight is that high order integral chained differentiator is used to estimate the newly defined variables and an adaptive neural controller is designed for the altitude subsystem where only one neural network is employed to approximate the lumped uncertain nonlinearity. The altitude subsystem controller is considerably simpler than the ones based on backstepping. It is proved using Lyapunov stability theory that the proposed control law can ensure that all the tracking error converges to an arbitrarily small neighborhood around zero. Numerical simulation study demonstrates the effectiveness of the proposed strategy, during the morphing process, in spite of some uncertain system nonlinearity. |
url |
http://dx.doi.org/10.1155/2015/787931 |
work_keys_str_mv |
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