Minimum Parameters Learning-Based Dynamic Surface Control for Advanced Aircraft at High Angle of Attack
Aiming at the difficulty of post-stall maneuvering control modeling and control of advanced aircraft under unsteady aerodynamics, a control method with high control accuracy and fast computation speed is proposed based on Radial Basis Function (RBF) network with minimum parameters learning (MPL) and...
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doaj-211c0ea2fd3b4c9a846fa47ca08c737e2021-03-29T23:41:20ZengIEEEIEEE Access2169-35362019-01-01714972414973510.1109/ACCESS.2019.29380138824183Minimum Parameters Learning-Based Dynamic Surface Control for Advanced Aircraft at High Angle of AttackJingping Shi0Yongxi Lyu1https://orcid.org/0000-0001-8912-3418Yuyan Cao2Huakun Chen3https://orcid.org/0000-0002-9184-9493Xiaobo Qu4School of Automation, Northwestern Polytechnical University, Xi’an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an, ChinaAiming at the difficulty of post-stall maneuvering control modeling and control of advanced aircraft under unsteady aerodynamics, a control method with high control accuracy and fast computation speed is proposed based on Radial Basis Function (RBF) network with minimum parameters learning (MPL) and dynamic surface control (DSC) method. Firstly, the aerodynamic characteristics of post-stall maneuvers are analyzed based on the experimental data of large-scale oscillation wind tunnels, and the key factors affecting the unsteady aerodynamic forces are obtained. Then, an accurate unsteady aerodynamic model is established based on the improved extreme learning machine (ELM) method. Secondly, the influence of unsteady aerodynamic forces on the control of post-stall maneuvers is considered. For the uncertainty of advanced aircraft model, high angle of attack flight control laws based on RBF-DSC are designed. In order to improve the calculation speed of the above control law and optimize the parameters, a post-stall maneuver control law method based on MPL-RBF-DSC is designed, and the stability of the method is proved. The coordinated allocation of the conventional aerodynamic surfaces and thrust vectors is realized based on the daisy chain method. Finally, the typical maneuver simulation of “Cobra” is carried out, which highlights the advantages of the design method in this paper, such as high control accuracy, short calculation time and strong robustness.https://ieeexplore.ieee.org/document/8824183/Flight controlhigh angle of attackwind tunnel testunsteady aerodynamics modelingMPL-RBF-DSC method |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jingping Shi Yongxi Lyu Yuyan Cao Huakun Chen Xiaobo Qu |
spellingShingle |
Jingping Shi Yongxi Lyu Yuyan Cao Huakun Chen Xiaobo Qu Minimum Parameters Learning-Based Dynamic Surface Control for Advanced Aircraft at High Angle of Attack IEEE Access Flight control high angle of attack wind tunnel test unsteady aerodynamics modeling MPL-RBF-DSC method |
author_facet |
Jingping Shi Yongxi Lyu Yuyan Cao Huakun Chen Xiaobo Qu |
author_sort |
Jingping Shi |
title |
Minimum Parameters Learning-Based Dynamic Surface Control for Advanced Aircraft at High Angle of Attack |
title_short |
Minimum Parameters Learning-Based Dynamic Surface Control for Advanced Aircraft at High Angle of Attack |
title_full |
Minimum Parameters Learning-Based Dynamic Surface Control for Advanced Aircraft at High Angle of Attack |
title_fullStr |
Minimum Parameters Learning-Based Dynamic Surface Control for Advanced Aircraft at High Angle of Attack |
title_full_unstemmed |
Minimum Parameters Learning-Based Dynamic Surface Control for Advanced Aircraft at High Angle of Attack |
title_sort |
minimum parameters learning-based dynamic surface control for advanced aircraft at high angle of attack |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Aiming at the difficulty of post-stall maneuvering control modeling and control of advanced aircraft under unsteady aerodynamics, a control method with high control accuracy and fast computation speed is proposed based on Radial Basis Function (RBF) network with minimum parameters learning (MPL) and dynamic surface control (DSC) method. Firstly, the aerodynamic characteristics of post-stall maneuvers are analyzed based on the experimental data of large-scale oscillation wind tunnels, and the key factors affecting the unsteady aerodynamic forces are obtained. Then, an accurate unsteady aerodynamic model is established based on the improved extreme learning machine (ELM) method. Secondly, the influence of unsteady aerodynamic forces on the control of post-stall maneuvers is considered. For the uncertainty of advanced aircraft model, high angle of attack flight control laws based on RBF-DSC are designed. In order to improve the calculation speed of the above control law and optimize the parameters, a post-stall maneuver control law method based on MPL-RBF-DSC is designed, and the stability of the method is proved. The coordinated allocation of the conventional aerodynamic surfaces and thrust vectors is realized based on the daisy chain method. Finally, the typical maneuver simulation of “Cobra” is carried out, which highlights the advantages of the design method in this paper, such as high control accuracy, short calculation time and strong robustness. |
topic |
Flight control high angle of attack wind tunnel test unsteady aerodynamics modeling MPL-RBF-DSC method |
url |
https://ieeexplore.ieee.org/document/8824183/ |
work_keys_str_mv |
AT jingpingshi minimumparameterslearningbaseddynamicsurfacecontrolforadvancedaircraftathighangleofattack AT yongxilyu minimumparameterslearningbaseddynamicsurfacecontrolforadvancedaircraftathighangleofattack AT yuyancao minimumparameterslearningbaseddynamicsurfacecontrolforadvancedaircraftathighangleofattack AT huakunchen minimumparameterslearningbaseddynamicsurfacecontrolforadvancedaircraftathighangleofattack AT xiaoboqu minimumparameterslearningbaseddynamicsurfacecontrolforadvancedaircraftathighangleofattack |
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1724189059761307648 |