Model Reference Adaptive Control of a Quadrotor UAV based on RBF Neural Networks

Mengqian Liu; Xiwang Dong; Qingdong Li; Zhang Ren

doi:10.1109/GNCC42960.2018.9019021

Model Reference Adaptive Control of a Quadrotor UAV based on RBF Neural Networks

Mengqian Liu
, Xiwang Dong
, Qingdong Li
, Zhang Ren

Beihang University

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

3 Scopus citations

Abstract

In this paper, a model reference control method based on RBF neural networks is applied to attitude control of quadrotor. The model of a quadrotor is constructed and simplified to obtain the reference model in the same order as the plant. The RBF is trained by using the gradient descent method. Through simulation experiments, MRAC based on RBF has presented good tracking performance on the nonlinear quadrotor system with unknown and changing parameters.

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/GNCC42960.2018.9019021
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

Access to Document

10.1109/GNCC42960.2018.9019021

Cite this

Liu, M., Dong, X., Li, Q., & Ren, Z. (2018). Model Reference Adaptive Control of a Quadrotor UAV based on RBF Neural Networks. In 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 Article 9019021 (2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GNCC42960.2018.9019021

@inproceedings{de115f574f2042db8567488eec4b0e9d,

title = "Model Reference Adaptive Control of a Quadrotor UAV based on RBF Neural Networks",

abstract = "In this paper, a model reference control method based on RBF neural networks is applied to attitude control of quadrotor. The model of a quadrotor is constructed and simplified to obtain the reference model in the same order as the plant. The RBF is trained by using the gradient descent method. Through simulation experiments, MRAC based on RBF has presented good tracking performance on the nonlinear quadrotor system with unknown and changing parameters.",

author = "Mengqian Liu and Xiwang Dong and Qingdong Li and Zhang Ren",

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/GNCC42960.2018.9019021",

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",

address = "美国",

}

Liu, M, Dong, X , Li, Q & Ren, Z 2018, Model Reference Adaptive Control of a Quadrotor UAV based on RBF Neural Networks. in 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018., 9019021, 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/GNCC42960.2018.9019021

Model Reference Adaptive Control of a Quadrotor UAV based on RBF Neural Networks. / Liu, Mengqian; Dong, Xiwang ; Li, Qingdong et al.
2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 9019021 (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 - Model Reference Adaptive Control of a Quadrotor UAV based on RBF Neural Networks

AU - Liu, Mengqian

AU - Dong, Xiwang

AU - Li, Qingdong

AU - Ren, Zhang

PY - 2018/8

Y1 - 2018/8

N2 - In this paper, a model reference control method based on RBF neural networks is applied to attitude control of quadrotor. The model of a quadrotor is constructed and simplified to obtain the reference model in the same order as the plant. The RBF is trained by using the gradient descent method. Through simulation experiments, MRAC based on RBF has presented good tracking performance on the nonlinear quadrotor system with unknown and changing parameters.

AB - In this paper, a model reference control method based on RBF neural networks is applied to attitude control of quadrotor. The model of a quadrotor is constructed and simplified to obtain the reference model in the same order as the plant. The RBF is trained by using the gradient descent method. Through simulation experiments, MRAC based on RBF has presented good tracking performance on the nonlinear quadrotor system with unknown and changing parameters.

UR - https://www.scopus.com/pages/publications/85082435782

U2 - 10.1109/GNCC42960.2018.9019021

DO - 10.1109/GNCC42960.2018.9019021

M3 - 会议稿件

AN - SCOPUS:85082435782

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 -

Model Reference Adaptive Control of a Quadrotor UAV based on RBF Neural Networks

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this