The Motion Controller Based on Neural Network S-Plane Model for Fixed-Wing UAVs

被引：5
|
作者：
Chen, Pengyun [1 ]
Zhang, Guobing [1 ]
Guan, Tong [1 ]
Yuan, Meini [1 ]
Shen, Jian [1 ]
机构：
[1] North Univ China, Coll Mechatron Engn, Taiyuan 030051, Peoples R China
来源：
IEEE ACCESS | 2021年 / 9卷
基金：
中国国家自然科学基金;
关键词：
Unmanned aerial vehicles; Mathematical model; Radial basis function networks; Neural networks; PD control; Adaptation models; Uncertainty; Fixed wing UAV; S-plane control; radial basis function neural network (RBFNN); adaptive adjustment; DESIGN;
D O I：
10.1109/ACCESS.2021.3093768
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Aiming at the attitude control problem of fixed wing UAV, this paper introduces S-plane control, which has good control effect in the field of underwater UAV, into the attitude control of UAV. At the same time, aiming at the problem that the coefficient setting of parameters in S-plane control completely depends on experience and cannot be adjusted adaptively, the radial basis function neural network (RBFNN) is introduced, and a neural network S-plane control model which can realize on-line adaptive adjustment of the coefficient of parameters in S-plane control is proposed. The simulation results based on the data of a certain UAV show that compared with the S-plane control, the proposed neural network S-plane control model has the characteristics of fast response speed, strong anti-interference ability, and strong robustness. In addition, it also has the function of adaptive adjustment, which shows good control performance.
引用
收藏
页码：93927 / 93936
页数：10
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