PID Control of Miniature Unmanned Helicopter Yaw System Based on RBF Neural Network

Yue Pan; Ping Song; Kejie Li

doi:10.1007/978-3-642-18134-4_50

PID Control of Miniature Unmanned Helicopter Yaw System Based on RBF Neural Network

Yue Pan, Ping Song^*, Kejie Li

^*Corresponding author for this work

School of Mechatronical Engineering

Beijing Institute of Technology

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

7 Citations (Scopus)

Abstract

The yaw dynamics of a miniature unmanned helicopter exhibits a complex, nonlinear, time-varying and coupling dynamic behavior. In this paper, simplified yaw dynamics model of MUH in hovering or low-velocity flight mode is established. The SISO model of yaw dynamics is obtained by mechanism modeling and system identification modeling method. PID control based on RBF neural network method combines the advantages of traditional PID controller and neural network controller. It has fast response, good robustness and self-adapting ability. It is suitable to control the yaw system of MUH. Simulation results show that the control system works well with quick response, good robustness and self adaptation.

Original language	English
Title of host publication	Intelligent Computing and Information Science
Subtitle of host publication	International Conference, ICIIS 2011 Chongqing, China, January 8-9, 2011 Proceedings, Part II
Editors	Ran Chen
Pages	308-313
Number of pages	6
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-18134-4_50
Publication status	Published - 2011
Event	2011 International Conference on Intelligent Computing and Information Science, ICICIS 2011 - Chongqing, China Duration: 8 Jan 2011 → 9 Jan 2011

Publication series

Name	Communications in Computer and Information Science
Number	PART 2
Volume	135
ISSN (Print)	1865-0929

Conference

Conference	2011 International Conference on Intelligent Computing and Information Science, ICICIS 2011
Country/Territory	China
City	Chongqing
Period	8/01/11 → 9/01/11

Keywords

PID
RBF neural network
Unmanned Helicopter
Yaw control

Access to Document

10.1007/978-3-642-18134-4_50

Cite this

Pan, Y., Song, P., & Li, K. (2011). PID Control of Miniature Unmanned Helicopter Yaw System Based on RBF Neural Network. In R. Chen (Ed.), Intelligent Computing and Information Science: International Conference, ICIIS 2011 Chongqing, China, January 8-9, 2011 Proceedings, Part II (PART 2 ed., pp. 308-313). (Communications in Computer and Information Science; Vol. 135, No. PART 2). https://doi.org/10.1007/978-3-642-18134-4_50

Pan, Yue ; Song, Ping ; Li, Kejie. / PID Control of Miniature Unmanned Helicopter Yaw System Based on RBF Neural Network. Intelligent Computing and Information Science: International Conference, ICIIS 2011 Chongqing, China, January 8-9, 2011 Proceedings, Part II. editor / Ran Chen. PART 2. ed. 2011. pp. 308-313 (Communications in Computer and Information Science; PART 2).

@inproceedings{2073a47199a54c74a349f29be7304b04,

title = "PID Control of Miniature Unmanned Helicopter Yaw System Based on RBF Neural Network",

abstract = "The yaw dynamics of a miniature unmanned helicopter exhibits a complex, nonlinear, time-varying and coupling dynamic behavior. In this paper, simplified yaw dynamics model of MUH in hovering or low-velocity flight mode is established. The SISO model of yaw dynamics is obtained by mechanism modeling and system identification modeling method. PID control based on RBF neural network method combines the advantages of traditional PID controller and neural network controller. It has fast response, good robustness and self-adapting ability. It is suitable to control the yaw system of MUH. Simulation results show that the control system works well with quick response, good robustness and self adaptation.",

keywords = "PID, RBF neural network, Unmanned Helicopter, Yaw control",

author = "Yue Pan and Ping Song and Kejie Li",

year = "2011",

doi = "10.1007/978-3-642-18134-4_50",

language = "English",

isbn = "9783642181337",

series = "Communications in Computer and Information Science",

number = "PART 2",

pages = "308--313",

editor = "Ran Chen",

booktitle = "Intelligent Computing and Information Science",

edition = "PART 2",

note = "2011 International Conference on Intelligent Computing and Information Science, ICICIS 2011 ; Conference date: 08-01-2011 Through 09-01-2011",

}

Pan, Y, Song, P & Li, K 2011, PID Control of Miniature Unmanned Helicopter Yaw System Based on RBF Neural Network. in R Chen (ed.), Intelligent Computing and Information Science: International Conference, ICIIS 2011 Chongqing, China, January 8-9, 2011 Proceedings, Part II. PART 2 edn, Communications in Computer and Information Science, no. PART 2, vol. 135, pp. 308-313, 2011 International Conference on Intelligent Computing and Information Science, ICICIS 2011, Chongqing, China, 8/01/11. https://doi.org/10.1007/978-3-642-18134-4_50

PID Control of Miniature Unmanned Helicopter Yaw System Based on RBF Neural Network. / Pan, Yue; Song, Ping; Li, Kejie.
Intelligent Computing and Information Science: International Conference, ICIIS 2011 Chongqing, China, January 8-9, 2011 Proceedings, Part II. ed. / Ran Chen. PART 2. ed. 2011. p. 308-313 (Communications in Computer and Information Science; Vol. 135, No. PART 2).

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

TY - GEN

T1 - PID Control of Miniature Unmanned Helicopter Yaw System Based on RBF Neural Network

AU - Pan, Yue

AU - Song, Ping

AU - Li, Kejie

PY - 2011

Y1 - 2011

N2 - The yaw dynamics of a miniature unmanned helicopter exhibits a complex, nonlinear, time-varying and coupling dynamic behavior. In this paper, simplified yaw dynamics model of MUH in hovering or low-velocity flight mode is established. The SISO model of yaw dynamics is obtained by mechanism modeling and system identification modeling method. PID control based on RBF neural network method combines the advantages of traditional PID controller and neural network controller. It has fast response, good robustness and self-adapting ability. It is suitable to control the yaw system of MUH. Simulation results show that the control system works well with quick response, good robustness and self adaptation.

AB - The yaw dynamics of a miniature unmanned helicopter exhibits a complex, nonlinear, time-varying and coupling dynamic behavior. In this paper, simplified yaw dynamics model of MUH in hovering or low-velocity flight mode is established. The SISO model of yaw dynamics is obtained by mechanism modeling and system identification modeling method. PID control based on RBF neural network method combines the advantages of traditional PID controller and neural network controller. It has fast response, good robustness and self-adapting ability. It is suitable to control the yaw system of MUH. Simulation results show that the control system works well with quick response, good robustness and self adaptation.

KW - PID

KW - RBF neural network

KW - Unmanned Helicopter

KW - Yaw control

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DO - 10.1007/978-3-642-18134-4_50

M3 - Conference contribution

AN - SCOPUS:84868018257

SN - 9783642181337

T3 - Communications in Computer and Information Science

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BT - Intelligent Computing and Information Science

A2 - Chen, Ran

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ER -

Pan Y, Song P, Li K. PID Control of Miniature Unmanned Helicopter Yaw System Based on RBF Neural Network. In Chen R, editor, Intelligent Computing and Information Science: International Conference, ICIIS 2011 Chongqing, China, January 8-9, 2011 Proceedings, Part II. PART 2 ed. 2011. p. 308-313. (Communications in Computer and Information Science; PART 2). doi: 10.1007/978-3-642-18134-4_50

PID Control of Miniature Unmanned Helicopter Yaw System Based on RBF Neural Network

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