Position Control of Quadrotor UAV Based on Cascade Fuzzy Neural Network

被引:31
作者
Rao, Jinjun [1 ]
Li, Bo [1 ]
Zhang, Zhen [1 ]
Chen, Dongdong [1 ]
Giernacki, Wojciech [2 ]
机构
[1] Shanghai Univ, Shanghai Key Lab Intelligent Mfg & Robot, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China
[2] Poznan Univ Tech, Fac Control, Inst Robot & Machine Intelligence Robot & Elect E, Piotrowo 3A, PL-60965 Poznan, Poland
基金
中国国家自然科学基金;
关键词
position control; fuzzy neural network; cascade control; trajectory tracking; UAV; TRACKING CONTROL; PAYLOAD;
D O I
10.3390/en15051763
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
In this article, a cascade fuzzy neural network (FNN) control approach is proposed for position control of quadrotor unmanned aerial vehicle (UAV) system with high coupling and underactuated. For the attitude loop with limited range, the FNN controller parameters were trained offline using flight data, whereas for the position loop, the method based on FNN compensation proportional-integral-derivative (PID) was adopted to tune the system online adaptively. This method not only combined the advantages of fuzzy systems and neural networks but also reduced the amount of calculation for cascade neural network control. Simulations of fixed set point flight and spiral and square trajectory tracking flight were then conducted. The comparison of the results showed that our method had advantages in terms of minimizing overshoot and settling time. Finally, flight experiments were carried out on a DJI Tello quadrotor UAV. The experimental results showed that the proposed controller had good performance in position control.
引用
收藏
页数:18
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