Adaptive Flight Control for Quadrotor UAVs with Dynamic Inversion and Neural Networks

Xiang, Tian1; Jiang, Fan1; Hao, Qi1; Cong, Wang2; IEEE

10.1109/MFI.2016.7849485

2016

978-1-4673-9709-4

2016 IEEE INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INTEGRATION FOR INTELLIGENT SYSTEMS (MFI)

0

174-179

19-21 Sept. 2016

Baden-Baden, Germany

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

In this paper, we develop an adaptive nonlinear controller based on dynamic inversion and neural network for quadrotor UAVs in the presence of uncertainties in UAV and actuator dynamics. The basic control law is first designed by using conventional PID control, and then the nonlinear dynamic inversion control is provided for the purpose of stabilization and robustness. The neural network is used to eliminate the inversion error due to parameter uncertainty, disturbance, etc. The simulation results demonstrate that NN shows better performance than PID control in the presence of the external disturbances which can improve the robustness of the whole system and achieve accurate attitude and trajectory control.

PI control PD control Uncertainty Artificial neural networks Attitude control Vehicle dynamics

第一 ; 通讯

英语

CPCI-S ; EI

South University of Science and Technology of China Research Committee[FRG-SUSTC1501A-29] ; South University of Science and Technology of China Research Committee[FRG-SUSTC1501A-44]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems

WOS:000405714400028

20171103451924

Intelligent systems ; Robustness (control systems) ; Three term control systems

Artificial Intelligence:723.4 ; Control Systems:731.1

Web of Science

1.Southern Univ Sci & Technol China, Dept Elect & Elect Engn, Shenzhen 518055, Guangdong, Peoples R China
2.South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China

Xiang, Tian,Jiang, Fan,Hao, Qi,et al. Adaptive Flight Control for Quadrotor UAVs with Dynamic Inversion and Neural Networks[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2016:174-179.