Lateral control law design for helicopter using radial basis function neural network

被引：3

作者：

Lu, Jingchao ^{[1
]}

Ling, Qiong ^{[1
]}

Zhang, Jiaming ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Dept Automat Control, Xian 710072, ShaanXi, Peoples R China

来源：

2007 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6 | 2007年

关键词：

T-S model; radial basis function neural network; parameter mapping; flight control;

D O I：

10.1109/ICAL.2007.4339059

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

As fixed-parameter control can not satisfy control requirements when helicopter is aviating in large scale flight envelop, this paper proposes a new control law design to adjust parameters on-line. Firstly a parameter-mapping approach is developed to design flight control parameters at certain flight conditions according to the desired system performance. Then parameters obtained at given conditions are used to train Radial Basis Function Neural Network (RBFNN). Thus RBFNN can generalize the given flight conditions information and output appropriate control parameters which will meet control requirements for any current flight condition within the given flight envelop. Simulation results indicate this control law design is feasible and effective.

引用

页码：2807 / 2812

页数：6

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