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^*

^*Corresponding author for this work

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

8 Citations (Scopus)

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.

Original language	English
Title of host publication	ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings
Publisher	IEEE Computer Society
Pages	580-584
Number of pages	5
Volume	2017-October
ISBN (Electronic)	9788993215137
DOIs	https://doi.org/10.23919/ICCAS.2017.8204300
Publication status	Published - 2017 Dec 13
Event	17th International Conference on Control, Automation and Systems, ICCAS 2017 - Jeju, Korea, Republic of Duration: 2017 Oct 18 → 2017 Oct 21

Other

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

Keywords

Flight control
Flying robot
Propellers
Quad-rotor
RBF neural network

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Control and Systems Engineering
Electrical and Electronic Engineering

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10.23919/ICCAS.2017.8204300

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Radial basis function neural network based PID control for quad-rotor flying robot. / Furukawa, Shoji; Kondo, Shunya; Takanishi, Atsuo et al.
ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings. Vol. 2017-October IEEE Computer Society, 2017. p. 580-584.

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

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",

month = dec,

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doi = "10.23919/ICCAS.2017.8204300",

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AU - Takanishi, Atsuo

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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

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KW - Propellers

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KW - RBF neural network

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

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

Abstract

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Keywords

ASJC Scopus subject areas

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