Prescribed performance dynamic neural network control for a flexible hypersonic vehicle with unknown control directions
In this article, we examine the system control design of a flexible hypersonic vehicle with an unknown direction control. Prescribed performance controls, backstepping controls, and radial basis function neural network are used to design the controllers. Different from a traditional radial basis fun...
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2019-04-01
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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/1687814019841489 |
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doaj-dc12cff6206142688e23cd0f569586922020-11-25T02:52:30ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402019-04-011110.1177/1687814019841489Prescribed performance dynamic neural network control for a flexible hypersonic vehicle with unknown control directionsHe-wei ZhaoYong LiangIn this article, we examine the system control design of a flexible hypersonic vehicle with an unknown direction control. Prescribed performance controls, backstepping controls, and radial basis function neural network are used to design the controllers. Different from a traditional radial basis function neural network, the fully tuned dynamic radial basis function neural network has a better approximation ability; and the weight vector, respective centers, and width of the Gaussian function of neural network are regulated by adaptive laws designed in the controller. The proof and analysis of stability are taken in this article for the fully tuned dynamic neural network introduced to control the system. Furthermore, prescribed performance control can guarantee the tracking errors satisfy the specified conditions. The unknown control direction is solved with the Nussbaum function in the controller. Finally, the simulations demonstrate the effectiveness and corrective measures of the control strategy.https://doi.org/10.1177/1687814019841489 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
He-wei Zhao Yong Liang |
spellingShingle |
He-wei Zhao Yong Liang Prescribed performance dynamic neural network control for a flexible hypersonic vehicle with unknown control directions Advances in Mechanical Engineering |
author_facet |
He-wei Zhao Yong Liang |
author_sort |
He-wei Zhao |
title |
Prescribed performance dynamic neural network control for a flexible hypersonic vehicle with unknown control directions |
title_short |
Prescribed performance dynamic neural network control for a flexible hypersonic vehicle with unknown control directions |
title_full |
Prescribed performance dynamic neural network control for a flexible hypersonic vehicle with unknown control directions |
title_fullStr |
Prescribed performance dynamic neural network control for a flexible hypersonic vehicle with unknown control directions |
title_full_unstemmed |
Prescribed performance dynamic neural network control for a flexible hypersonic vehicle with unknown control directions |
title_sort |
prescribed performance dynamic neural network control for a flexible hypersonic vehicle with unknown control directions |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8140 |
publishDate |
2019-04-01 |
description |
In this article, we examine the system control design of a flexible hypersonic vehicle with an unknown direction control. Prescribed performance controls, backstepping controls, and radial basis function neural network are used to design the controllers. Different from a traditional radial basis function neural network, the fully tuned dynamic radial basis function neural network has a better approximation ability; and the weight vector, respective centers, and width of the Gaussian function of neural network are regulated by adaptive laws designed in the controller. The proof and analysis of stability are taken in this article for the fully tuned dynamic neural network introduced to control the system. Furthermore, prescribed performance control can guarantee the tracking errors satisfy the specified conditions. The unknown control direction is solved with the Nussbaum function in the controller. Finally, the simulations demonstrate the effectiveness and corrective measures of the control strategy. |
url |
https://doi.org/10.1177/1687814019841489 |
work_keys_str_mv |
AT heweizhao prescribedperformancedynamicneuralnetworkcontrolforaflexiblehypersonicvehiclewithunknowncontroldirections AT yongliang prescribedperformancedynamicneuralnetworkcontrolforaflexiblehypersonicvehiclewithunknowncontroldirections |
_version_ |
1724729577753804800 |