DocumentCode
358940
Title
Dynamically structured radial basis function neural networks for robust aircraft flight control
Author
Yan, Li ; Sundarajan, N. ; Saratchandran, P.
Author_Institution
Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore
Volume
5
fYear
2000
fDate
2000
Firstpage
3501
Abstract
An online control scheme that utilizes a dynamically structured radial basis function network (RBFN) is developed for aircraft control. By using Lyapunov synthesis approach, the tuning rule for updating all the parameters of the dynamic RBFN which guarantees the stability of the overall system is derived. The robustness of the proposed tuning rule is also analyzed. Simulation studies using the F8 aircraft longitudinal model demonstrates the efficiency of the method and also show that with a dynamically structured RBFN, a more compact network structure can be implemented
Keywords
Lyapunov methods; aircraft control; control system synthesis; military aircraft; neurocontrollers; nonlinear control systems; online operation; radial basis function networks; robust control; F8 aircraft longitudinal model; Lyapunov synthesis; RBFN; dynamically structured radial basis function neural networks; online control scheme; robust aircraft flight control; simulation studies; Aerospace control; Aerospace electronics; Aircraft propulsion; Control systems; Military aircraft; Neural networks; Nonlinear systems; Radial basis function networks; Robust control; Robust stability;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 2000. Proceedings of the 2000
Conference_Location
Chicago, IL
ISSN
0743-1619
Print_ISBN
0-7803-5519-9
Type
conf
DOI
10.1109/ACC.2000.879220
Filename
879220
Link To Document