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Image of Vibration Testing and System Dynamics
ISSN: 2475-4811 (print)
ISSN: 2475-482X (online)
Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland

Email: pawel.olejnik@p.lodz.pl

C. Steve Suh (editor)

Texas A&M University, USA

Email: ssuh@tamu.edu

Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China

Email: tuoxianguo@suse.edu.cn

The Trajectory Tracking Control of fixed-wing UAV with Self-organizing Fuzzy Neural Network to Identify and Compensate the Modelling Uncertainties

Journal of Vcibration Testing and System Dynamics 3(1) (2019) 91--107 | DOI:10.5890/JVTSD.2019.03.007

Yu Tang$^{1}$,$^{2}$,$^{3}$, Lijia Cao$^{1}$,$^{2}$,$^{3}$, Da Lin$^{1}$,$^{2}$

$^{1}$ Artificial Intelligence Key Laboratory of Sichuan Province, Zigong, 643000, P.R. China

$^{2}$ Sichuan University of Science & Engineering, Zigong, 643000, P.R. China

$^{3}$ Sichuan Key Provincial Research Base of Intelligent Tourism, Zigong, 643000, P.R. China

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Abstract

Firstly, the dynamics model of fixed-wing UAV is given in the paper and an adaptive controller of the mode is designed based on dynamic inversion method, then a compensation strategy called SFNN-RC based on the self-organizing fuzzy neural network techniques and a robust controller is designed to online identify and compensate the modeling uncertainty part of fixed-wing UAV system in trajectory tracking control process. The robust controller in SFNN-RC is presented to optimize the convergence performance of the self-organizing fuzzy neural network. The stability of the SFNN-RC method can be proved based on Lyapunov theory. To demonstrate the effectiveness of the proposed method, some simulation results are illustrated in this paper.

Acknowledgments

This project is supported by the National Natural Science Foundation of China (Grants 61640223 and 11705122), the National Aerospace Science Foundation of China (Grant 201605U8002), the Open Project Program of the State Key Laboratory of Management and Control for Complex Systems (Grant 20160106), Sichuan Science and Technology Program (Grants 19ZDZX0037, 2018JY0512, 2016JY0179), the Innovation Group Build Plan for the Universities of Sichuan Province (Grant 15TD0024), the Key Project of Artificial Intelligence Key Laboratory of Sichuan Province (Grants 2016RYJ02 and 2015RYJ02), the Natural Science Foundation of Sichuan University of Science & Engineering (Grant 2018RCL18) and Sichuan Key Provincial Research Base of Intelligent Tourism Foundation (Grant ZHZJ18-01).

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