Time-Domain Identification Method Based on Data-Driven Intelligent Correction of Aerodynamic Parameters of Fixed-Wing UAV

被引:0
|
作者
Yang, Dapeng [1 ,2 ]
Zang, Jianwen [3 ]
Liu, Jun [3 ]
Liu, Kai [3 ]
机构
[1] Fudan Univ, Dept Aeronaut & Astronaut, Shanghai 200433, Peoples R China
[2] Shenyang Aircraft Design & Res Inst, Shenyang 110035, Peoples R China
[3] Dalian Univ Technol, Sch Aeronaut & Astronaut, Dalian 116024, Peoples R China
基金
中国国家自然科学基金;
关键词
time-domain identification method; data-driven; intelligent correction; aerodynamic parameters; fixed-wing UAV;
D O I
10.3390/drones7090594
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
In order to overcome the influence of complex environmental disturbance factors such as nonlinear time-varying characteristics on the dynamic control performance of small fixed-wing UAVs, the nonlinear expression relationship of neural networks (NNs) is combined with the recursive least squares (RLSs) identification algorithm. This paper proposes a hybrid aerodynamic parameter identification method based on NN-RLS offline network training and online learning correction. The simulation results show that compared with the real value of the identification value obtained by this algorithm, the residual error of the moment coefficient is reduced by 69%, and the residual error of the force coefficient is reduced by 89%. Under the same identification accuracy, the identification time is shortened from the original 0.1 s to 0.01 s. Compared with traditional identification algorithms, better estimation results can be obtained. By using this algorithm to continuously update the NN model and iterate repeatedly, iterative learning for complex dynamic models can be realized, providing support for the optimization of UAV control schemes.
引用
收藏
页数:20
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