Adaptive neuro-fuzzy inference system design of inverted pendulum system on an inclined rail

Xianran Jia; Yaping Dai; Zubair Ahmed Memon

doi:10.1109/GCIS.2010.67

Adaptive neuro-fuzzy inference system design of inverted pendulum system on an inclined rail

Xianran Jia^*
, Yaping Dai
, Zubair Ahmed Memon

^*Corresponding author for this work

School of Automation

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

8 Citations (Scopus)

Abstract

The basic aim of our work was to design appropriate controller to control the angle of the pendulum and the position of the cart in order to stabilize the conventional inverted pendulum system on an inclined rail. We improved the adaptive neuro-fuzzy inference system (ANFIS) on the basis of conventional fuzzy controller. A neuro-fuzzy hybrid approach was used to design the fuzzy rule base on a basis of building a Sugeno fuzzy model in order to swing a pendulum attached to a cart from an initial downwards position to an upright position and maintain that state. The adaptive neuro-fuzzy logic controller was designed in the Matlab-Simulink environment. By training and checking of effective data, the results proved that the adaptive neuro-fuzzy controller had good performance about stability in the realtime control of the inverted pendulum on an inclined rail.

Original language	English
Title of host publication	Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010
Pages	137-141
Number of pages	5
DOIs	https://doi.org/10.1109/GCIS.2010.67
Publication status	Published - 2010
Event	2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010 - Wuhan, China Duration: 16 Dec 2010 → 17 Dec 2010

Publication series

Name	Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010
Volume	3

Conference

Conference	2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010
Country/Territory	China
City	Wuhan
Period	16/12/10 → 17/12/10

Keywords

Adaptive neuro-fuzzy inference system (ANFIS)
Inverted pendulum system on an inclined rail

Access to Document

10.1109/GCIS.2010.67

Cite this

Jia, X., Dai, Y., & Memon, Z. A. (2010). Adaptive neuro-fuzzy inference system design of inverted pendulum system on an inclined rail. In Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010 (pp. 137-141). Article 5709341 (Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010; Vol. 3). https://doi.org/10.1109/GCIS.2010.67

@inproceedings{d3d63ae3c8364365913b67786e27101d,

title = "Adaptive neuro-fuzzy inference system design of inverted pendulum system on an inclined rail",

abstract = "The basic aim of our work was to design appropriate controller to control the angle of the pendulum and the position of the cart in order to stabilize the conventional inverted pendulum system on an inclined rail. We improved the adaptive neuro-fuzzy inference system (ANFIS) on the basis of conventional fuzzy controller. A neuro-fuzzy hybrid approach was used to design the fuzzy rule base on a basis of building a Sugeno fuzzy model in order to swing a pendulum attached to a cart from an initial downwards position to an upright position and maintain that state. The adaptive neuro-fuzzy logic controller was designed in the Matlab-Simulink environment. By training and checking of effective data, the results proved that the adaptive neuro-fuzzy controller had good performance about stability in the realtime control of the inverted pendulum on an inclined rail.",

keywords = "Adaptive neuro-fuzzy inference system (ANFIS), Inverted pendulum system on an inclined rail",

author = "Xianran Jia and Yaping Dai and Memon, \{Zubair Ahmed\}",

year = "2010",

doi = "10.1109/GCIS.2010.67",

language = "English",

isbn = "9780769543048",

series = "Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010",

pages = "137--141",

booktitle = "Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010",

note = "2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010 ; Conference date: 16-12-2010 Through 17-12-2010",

}

Jia, X, Dai, Y & Memon, ZA 2010, Adaptive neuro-fuzzy inference system design of inverted pendulum system on an inclined rail. in Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010., 5709341, Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010, vol. 3, pp. 137-141, 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010, Wuhan, China, 16/12/10. https://doi.org/10.1109/GCIS.2010.67

Adaptive neuro-fuzzy inference system design of inverted pendulum system on an inclined rail. / Jia, Xianran; Dai, Yaping; Memon, Zubair Ahmed.
Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010. 2010. p. 137-141 5709341 (Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010; Vol. 3).

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

TY - GEN

T1 - Adaptive neuro-fuzzy inference system design of inverted pendulum system on an inclined rail

AU - Jia, Xianran

AU - Dai, Yaping

AU - Memon, Zubair Ahmed

PY - 2010

Y1 - 2010

N2 - The basic aim of our work was to design appropriate controller to control the angle of the pendulum and the position of the cart in order to stabilize the conventional inverted pendulum system on an inclined rail. We improved the adaptive neuro-fuzzy inference system (ANFIS) on the basis of conventional fuzzy controller. A neuro-fuzzy hybrid approach was used to design the fuzzy rule base on a basis of building a Sugeno fuzzy model in order to swing a pendulum attached to a cart from an initial downwards position to an upright position and maintain that state. The adaptive neuro-fuzzy logic controller was designed in the Matlab-Simulink environment. By training and checking of effective data, the results proved that the adaptive neuro-fuzzy controller had good performance about stability in the realtime control of the inverted pendulum on an inclined rail.

AB - The basic aim of our work was to design appropriate controller to control the angle of the pendulum and the position of the cart in order to stabilize the conventional inverted pendulum system on an inclined rail. We improved the adaptive neuro-fuzzy inference system (ANFIS) on the basis of conventional fuzzy controller. A neuro-fuzzy hybrid approach was used to design the fuzzy rule base on a basis of building a Sugeno fuzzy model in order to swing a pendulum attached to a cart from an initial downwards position to an upright position and maintain that state. The adaptive neuro-fuzzy logic controller was designed in the Matlab-Simulink environment. By training and checking of effective data, the results proved that the adaptive neuro-fuzzy controller had good performance about stability in the realtime control of the inverted pendulum on an inclined rail.

KW - Adaptive neuro-fuzzy inference system (ANFIS)

KW - Inverted pendulum system on an inclined rail

UR - https://www.scopus.com/pages/publications/79952408213

U2 - 10.1109/GCIS.2010.67

DO - 10.1109/GCIS.2010.67

M3 - Conference contribution

AN - SCOPUS:79952408213

SN - 9780769543048

T3 - Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010

SP - 137

EP - 141

BT - Proceedings - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010

T2 - 2010 2nd WRI Global Congress on Intelligent Systems, GCIS 2010

Y2 - 16 December 2010 through 17 December 2010

ER -

Adaptive neuro-fuzzy inference system design of inverted pendulum system on an inclined rail

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this