Radial basis function neural network based PID control for quad-rotor flying robot

Shoji Furukawa; Shunya Kondo; Atsuo Takanishi; Hun Ok Lim

doi:10.23919/ICCAS.2017.8204300

Radial basis function neural network based PID control for quad-rotor flying robot

Shoji Furukawa, Shunya Kondo, Atsuo Takanishi, Hun Ok Lim^*

^*この研究の対応する著者

研究成果: Conference contribution

8 被引用数 (Scopus)

抄録

It is difficult for flying robots with a conventional PID controller to fly stably with external disturbances such as wind. Thus, a flight control method that can change the control parameters of a conventional PID controller according to the external disturbances is described in this paper. The control parameters of the PID controller are automatically adjusted based on a radial basis function neural network (RBFNN). The experimental results show that the control method is capable of effectively dealing with external disturbances.

本文言語	English
ホスト出版物のタイトル	ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings
出版社	IEEE Computer Society
ページ	580-584
ページ数	5
巻	2017-October
ISBN（電子版）	9788993215137
DOI	https://doi.org/10.23919/ICCAS.2017.8204300
出版ステータス	Published - 2017 12月 13
イベント	17th International Conference on Control, Automation and Systems, ICCAS 2017 - Jeju, Korea, Republic of 継続期間: 2017 10月 18 → 2017 10月 21

Other

Other	17th International Conference on Control, Automation and Systems, ICCAS 2017
国/地域	Korea, Republic of
City	Jeju
Period	17/10/18 → 17/10/21

ASJC Scopus subject areas

人工知能
コンピュータサイエンスの応用
制御およびシステム工学
電子工学および電気工学

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10.23919/ICCAS.2017.8204300

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引用スタイル

Furukawa, S, Kondo, S, Takanishi, A & Lim, HO 2017, Radial basis function neural network based PID control for quad-rotor flying robot. in ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings. vol. 2017-October, IEEE Computer Society, pp. 580-584, 17th International Conference on Control, Automation and Systems, ICCAS 2017, Jeju, Korea, Republic of, 17/10/18. https://doi.org/10.23919/ICCAS.2017.8204300

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title = "Radial basis function neural network based PID control for quad-rotor flying robot",

abstract = "It is difficult for flying robots with a conventional PID controller to fly stably with external disturbances such as wind. Thus, a flight control method that can change the control parameters of a conventional PID controller according to the external disturbances is described in this paper. The control parameters of the PID controller are automatically adjusted based on a radial basis function neural network (RBFNN). The experimental results show that the control method is capable of effectively dealing with external disturbances.",

keywords = "Flight control, Flying robot, Propellers, Quad-rotor, RBF neural network",

author = "Shoji Furukawa and Shunya Kondo and Atsuo Takanishi and Lim, {Hun Ok}",

year = "2017",

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doi = "10.23919/ICCAS.2017.8204300",

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booktitle = "ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings",

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AU - Furukawa, Shoji

AU - Kondo, Shunya

AU - Takanishi, Atsuo

AU - Lim, Hun Ok

PY - 2017/12/13

Y1 - 2017/12/13

N2 - It is difficult for flying robots with a conventional PID controller to fly stably with external disturbances such as wind. Thus, a flight control method that can change the control parameters of a conventional PID controller according to the external disturbances is described in this paper. The control parameters of the PID controller are automatically adjusted based on a radial basis function neural network (RBFNN). The experimental results show that the control method is capable of effectively dealing with external disturbances.

AB - It is difficult for flying robots with a conventional PID controller to fly stably with external disturbances such as wind. Thus, a flight control method that can change the control parameters of a conventional PID controller according to the external disturbances is described in this paper. The control parameters of the PID controller are automatically adjusted based on a radial basis function neural network (RBFNN). The experimental results show that the control method is capable of effectively dealing with external disturbances.

KW - Flight control

KW - Flying robot

KW - Propellers

KW - Quad-rotor

KW - RBF neural network

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DO - 10.23919/ICCAS.2017.8204300

M3 - Conference contribution

AN - SCOPUS:85044459348

VL - 2017-October

SP - 580

EP - 584

BT - ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings

PB - IEEE Computer Society

T2 - 17th International Conference on Control, Automation and Systems, ICCAS 2017

Y2 - 18 October 2017 through 21 October 2017

ER -

Radial basis function neural network based PID control for quad-rotor flying robot

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