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JACIII Vol.5 No.1 pp. 58-70
doi: 10.20965/jaciii.2001.p0058
(2001)

Paper:

A New Fuzzy Controller for Stabilizing Inverted Pendulums Based on Single Input Rule Modules Dynamically Connected Fuzzy Inference Model

Jianqiang Yi*, Naoyoshi Yubazaki** and Kaoru Hirota***

*Institute of Automation, Chinese Academy of Sciences, P.O.Box 2728, Beijing 1ccc80, China

**Technology Research Center, Mycom, Inc. 12, S. Shimobano, Saga Hirosawa, Ukyo, Kyoto 616-8303, Japan

***Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori, Yokohama 226-8502, Japan

Received:
September 1, 1999
Accepted:
January 9, 2001
Published:
January 20, 2001
Keywords:
Dynamic importance degree, Fuzzy control, Inverted pendulum, Single input rule module, Stabilization
Abstract

A fuzzy controller is presented based on the Single Input Rule Modules (SIRMs) dynamically connected fuzzy inference model for stabilization control of inverted pendulums. The angle and angular velocity of the pendulum and the position and velocity of the cart are selected as input items and the driving force as the output item. By using SIRMs and dynamic importance degrees, the fuzzy controller realizes angular control of the pendulum and position control of the cart in parallel with totally only 24 fuzzy rules. Switching between angular control of the pendulum and position control of the cart is smoothly performed by automatically adjusting dynamic importance degrees according to control situations. For any inverted pendulums, of which the pendulum length is among [0.5m, 2.2m], simulation results show that the proposed fuzzy controller has a high generalization ability to stabilize the pendulum systems completely in about 6.0 seconds when the initial angle of the pendulum is among [-30.0°, +30.0°], or the initial position of the cart is among [-2.1m, +2.1m].

Cite this article as:
Jianqiang Yi, Naoyoshi Yubazaki, and Kaoru Hirota, “A New Fuzzy Controller for Stabilizing Inverted Pendulums Based on Single Input Rule Modules Dynamically Connected Fuzzy Inference Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.5, No.1, pp. 58-70, 2001.
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