Robust Adaptive Back-stepping Control Design Based on RBFNN for Morphing Aircraft

Fuxiang Qiao; Weiguo Zhang; Guangwen Li; Jingping Shi; Xiaobo Qu; Jun Che; Haijun Zhou

doi:10.1109/GNCC42960.2018.9018937

Robust Adaptive Back-stepping Control Design Based on RBFNN for Morphing Aircraft

Fuxiang Qiao, Weiguo Zhang, Guangwen Li, Jingping Shi, Xiaobo Qu, Jun Che, Haijun Zhou

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

This paper presents a robust adaptive back-stepping control base on radial basis function neural networks (RBFNN) for the nonlinear morphing aircraft. The adaptive term constructed by the RBFNN approximate the uncertainties of the system, and the robust term is designed to eliminate approximation error between the real value and evaluated value approximated by RBFNN. The performance and stability of controller are guaranteed by these two terms. It is proved by means of Lyapunov theory that the track error can be convergent and the signals are uniformly bounded. Simulation results show that the proposed controller can ensure good tracing performance of the morphing aircraft and suppress uncertainties of the system effectively.

Original language	English
Title of host publication	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538611715
DOIs	https://doi.org/10.1109/GNCC42960.2018.9018937
State	Published - Aug 2018
Event	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 - Xiamen, China Duration: 10 Aug 2018 → 12 Aug 2018

Publication series

Name	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

Conference

Conference	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
Country/Territory	China
City	Xiamen
Period	10/08/18 → 12/08/18

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10.1109/GNCC42960.2018.9018937

Cite this

Qiao, F., Zhang, W., Li, G., Shi, J., Qu, X., Che, J., & Zhou, H. (2018). Robust Adaptive Back-stepping Control Design Based on RBFNN for Morphing Aircraft. In 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 Article 9018937 (2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GNCC42960.2018.9018937

@inproceedings{19d8dc5d7ba74896b987d8414f0e1292,

title = "Robust Adaptive Back-stepping Control Design Based on RBFNN for Morphing Aircraft",

abstract = "This paper presents a robust adaptive back-stepping control base on radial basis function neural networks (RBFNN) for the nonlinear morphing aircraft. The adaptive term constructed by the RBFNN approximate the uncertainties of the system, and the robust term is designed to eliminate approximation error between the real value and evaluated value approximated by RBFNN. The performance and stability of controller are guaranteed by these two terms. It is proved by means of Lyapunov theory that the track error can be convergent and the signals are uniformly bounded. Simulation results show that the proposed controller can ensure good tracing performance of the morphing aircraft and suppress uncertainties of the system effectively.",

author = "Fuxiang Qiao and Weiguo Zhang and Guangwen Li and Jingping Shi and Xiaobo Qu and Jun Che and Haijun Zhou",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 ; Conference date: 10-08-2018 Through 12-08-2018",

year = "2018",

month = aug,

doi = "10.1109/GNCC42960.2018.9018937",

language = "英语",

series = "2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018",

}

Qiao, F, Zhang, W, Li, G, Shi, J, Qu, X, Che, J & Zhou, H 2018, Robust Adaptive Back-stepping Control Design Based on RBFNN for Morphing Aircraft. in 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018., 9018937, 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018, Xiamen, China, 10/08/18. https://doi.org/10.1109/GNCC42960.2018.9018937

Robust Adaptive Back-stepping Control Design Based on RBFNN for Morphing Aircraft. / Qiao, Fuxiang; Zhang, Weiguo; Li, Guangwen et al.
2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 9018937 (2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Robust Adaptive Back-stepping Control Design Based on RBFNN for Morphing Aircraft

AU - Qiao, Fuxiang

AU - Zhang, Weiguo

AU - Li, Guangwen

AU - Shi, Jingping

AU - Qu, Xiaobo

AU - Che, Jun

AU - Zhou, Haijun

PY - 2018/8

Y1 - 2018/8

N2 - This paper presents a robust adaptive back-stepping control base on radial basis function neural networks (RBFNN) for the nonlinear morphing aircraft. The adaptive term constructed by the RBFNN approximate the uncertainties of the system, and the robust term is designed to eliminate approximation error between the real value and evaluated value approximated by RBFNN. The performance and stability of controller are guaranteed by these two terms. It is proved by means of Lyapunov theory that the track error can be convergent and the signals are uniformly bounded. Simulation results show that the proposed controller can ensure good tracing performance of the morphing aircraft and suppress uncertainties of the system effectively.

AB - This paper presents a robust adaptive back-stepping control base on radial basis function neural networks (RBFNN) for the nonlinear morphing aircraft. The adaptive term constructed by the RBFNN approximate the uncertainties of the system, and the robust term is designed to eliminate approximation error between the real value and evaluated value approximated by RBFNN. The performance and stability of controller are guaranteed by these two terms. It is proved by means of Lyapunov theory that the track error can be convergent and the signals are uniformly bounded. Simulation results show that the proposed controller can ensure good tracing performance of the morphing aircraft and suppress uncertainties of the system effectively.

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U2 - 10.1109/GNCC42960.2018.9018937

DO - 10.1109/GNCC42960.2018.9018937

M3 - 会议稿件

AN - SCOPUS:85082477094

T3 - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

BT - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

Y2 - 10 August 2018 through 12 August 2018

ER -

Qiao F, Zhang W, Li G, Shi J, Qu X, Che J et al. Robust Adaptive Back-stepping Control Design Based on RBFNN for Morphing Aircraft. In 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 9018937. (2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018). doi: 10.1109/GNCC42960.2018.9018937

Robust Adaptive Back-stepping Control Design Based on RBFNN for Morphing Aircraft

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