Dynamically structured radial basis function neural networks for robust aircraft flight control

被引：0

作者：

Yan, L ^{[1
]}

Sundararajan, N ^{[1
]}

Saratchandran, P ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

PROCEEDINGS OF THE 2000 AMERICAN CONTROL CONFERENCE, VOLS 1-6 | 2000年

关键词：

flight control; radial basis function network (RBFN); feedback-error-learning; robustness; stability;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An on-line 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.

引用

页码：3501 / 3505

页数：5

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