Abstract
A back propagation neural network (BPNN) based PID control strategy for the attitude of quadrotor is proposed in this paper. Firstly, the architecture and dynamic model of quadrotor are analyzed according to the Newton-Euler Equation. Secondly, a nonlinear attitude model is established on the basis of the mathematical analysis. Thirdly, by eliminating the inverse error adaptively, a BPNN based PID controller is introduced to improve the robustness. Furthermore, PID parameters are adaptively adjusted through the training of neural network weighted coefficients. Finally, numerical examples demonstrate the performance of the designed BPNN based PID controller in terms of precision, adaptability and robustness.
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Acknowledgments
The work was supported in part by Natural Science Foundation of Jiangsu Province of China under Grant No. BK20130471 and No. BK20140638, China Postdoctoral Science Foundation under grant No. 2013M540404, Jiangsu Planned Projects for Postdoctoral Research Funds under grant No. 1401037B, open fund of Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education under Grant No. MCCSE2013B01, the Open Project Program of Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University (No. CDLS-2014-04), and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and the Fundamental Research Funds for the Central Universities.
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Zhao, D., Sun, C., Wang, Q., Yang, W. (2015). Neural Network Based PID Control for Quadrotor Aircraft. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_28
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DOI: https://doi.org/10.1007/978-3-319-23862-3_28
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