Nearspace vehicle control based on RBFNN

Author

He, Naibao ; Gao, Qian ; Liu, Yongqiang ; Gong, Chenglong ; Jiang, Changsheng

Author_Institution

Huaihai Inst. of Techology, Lianyungang, China

fYear

2010

fDate

26-28 May 2010

Firstpage

959

Lastpage

961

Abstract

With the assumption that NSV suffers the violent changes of aerodynamic paameters and the outside disturbance in hypersonic condition, we present the integrator backstepping approach based on fully tuned radial basis function neural network (FTRBFNN). The strict proof of the approach´s stability is provided simutanously. The performance analysis for the approach demonstrate that the FTRBFNN has better ability of restaining disturbance than RBFNN, and the integrator term in backstepping approach eliminate the static traking error efficiently.

Keywords

aerodynamics; hypersonic flow; neurocontrollers; radial basis function networks; space vehicles; stability; aerodynamic paameter; fully tuned radial basis function neural network; hypersonic condition; integrator backstepping; nearspace vehicle control; restaining disturbance; stability; static traking error; Aerodynamics; Automatic control; Automotive engineering; Backstepping; Educational institutions; Electronic mail; Helium; Radial basis function networks; Stability; Vehicles; backstepping approach; nearspace vehicle; neural network; robust control;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498084

Filename

5498084