Research on RBV Control Strategy of Large Angle Maneuver

Considering the hypersonic aerospace vehicle, with high dynamic, strong varying parameters, strong nonlinear, strong coupling, and the complicated flight environment, conventional flight control methods based on linear system may become invalid. To the high precision and reliable control problem of...

Full description

Bibliographic Details
Main Authors: Jiangtao Xu, Na Luo, Yu Fu, Litong Wang, Xiande Wu
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2014/718125
id doaj-e85d9f44ac484f1f95fa391233f002e6
record_format Article
spelling doaj-e85d9f44ac484f1f95fa391233f002e62020-11-24T21:21:48ZengHindawi LimitedAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/718125718125Research on RBV Control Strategy of Large Angle ManeuverJiangtao Xu0Na Luo1Yu Fu2Litong Wang3Xiande Wu4Department of Astronautics Engineering, Harbin Engineering University, Harbin 150001, ChinaDepartment of Astronautics Engineering, Harbin Engineering University, Harbin 150001, ChinaBeijing Institute of Astronautical Systems Engineering, Beijing 100076, ChinaDepartment of Astronautics Engineering, Harbin Engineering University, Harbin 150001, ChinaDepartment of Astronautics Engineering, Harbin Engineering University, Harbin 150001, ChinaConsidering the hypersonic aerospace vehicle, with high dynamic, strong varying parameters, strong nonlinear, strong coupling, and the complicated flight environment, conventional flight control methods based on linear system may become invalid. To the high precision and reliable control problem of this vehicle, nonlinear flight control strategy based on neural network robust adaptive dynamic inversion is proposed. Firstly, considering the nonlinear characteristics of aerodynamic coefficients varying with Mach numbers, attack angle, and sideslip angle, the complete nonlinear 6-DOF model of RBV is established. Secondly, based on the time-scale separation, using the nonlinear dynamic inversion control strategy achieves the pseudolinear decoupling of RBV. And then, using the neural network with single hidden layer approximates the dynamic inversion error for system model uncertainty. Next, the external disturbance and network approximating error are suppressed by robust adaptive control. Finally, using Lyapunov’s theory proves that all error signals of closed loop system are uniformly bounded finally under this control strategy. Nonlinear simulation verifies the feasibility and validity of this control strategy to the RBV control system.http://dx.doi.org/10.1155/2014/718125
collection DOAJ
language English
format Article
sources DOAJ
author Jiangtao Xu
Na Luo
Yu Fu
Litong Wang
Xiande Wu
spellingShingle Jiangtao Xu
Na Luo
Yu Fu
Litong Wang
Xiande Wu
Research on RBV Control Strategy of Large Angle Maneuver
Abstract and Applied Analysis
author_facet Jiangtao Xu
Na Luo
Yu Fu
Litong Wang
Xiande Wu
author_sort Jiangtao Xu
title Research on RBV Control Strategy of Large Angle Maneuver
title_short Research on RBV Control Strategy of Large Angle Maneuver
title_full Research on RBV Control Strategy of Large Angle Maneuver
title_fullStr Research on RBV Control Strategy of Large Angle Maneuver
title_full_unstemmed Research on RBV Control Strategy of Large Angle Maneuver
title_sort research on rbv control strategy of large angle maneuver
publisher Hindawi Limited
series Abstract and Applied Analysis
issn 1085-3375
1687-0409
publishDate 2014-01-01
description Considering the hypersonic aerospace vehicle, with high dynamic, strong varying parameters, strong nonlinear, strong coupling, and the complicated flight environment, conventional flight control methods based on linear system may become invalid. To the high precision and reliable control problem of this vehicle, nonlinear flight control strategy based on neural network robust adaptive dynamic inversion is proposed. Firstly, considering the nonlinear characteristics of aerodynamic coefficients varying with Mach numbers, attack angle, and sideslip angle, the complete nonlinear 6-DOF model of RBV is established. Secondly, based on the time-scale separation, using the nonlinear dynamic inversion control strategy achieves the pseudolinear decoupling of RBV. And then, using the neural network with single hidden layer approximates the dynamic inversion error for system model uncertainty. Next, the external disturbance and network approximating error are suppressed by robust adaptive control. Finally, using Lyapunov’s theory proves that all error signals of closed loop system are uniformly bounded finally under this control strategy. Nonlinear simulation verifies the feasibility and validity of this control strategy to the RBV control system.
url http://dx.doi.org/10.1155/2014/718125
work_keys_str_mv AT jiangtaoxu researchonrbvcontrolstrategyoflargeanglemaneuver
AT naluo researchonrbvcontrolstrategyoflargeanglemaneuver
AT yufu researchonrbvcontrolstrategyoflargeanglemaneuver
AT litongwang researchonrbvcontrolstrategyoflargeanglemaneuver
AT xiandewu researchonrbvcontrolstrategyoflargeanglemaneuver
_version_ 1725998135758553088