JRM Vol.7 No.1 pp. 36-44
doi: 10.20965/jrm.1995.p0036


Fuzzy Control for Inverted Pendulum Using Fuzzy Neural Networks

Shin-ichi Horikawa*, Masahiro Yamaguchi**, Takeshi Furuhashi*
and Yoshiki Uchikawa*

*Department of Information Electronics, Faculty of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-01 Japan

**Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma-shi, Nara, 630-01 Japan

December 22, 1994
January 10, 1995
February 20, 1995
Fuzzy neural network, Inverted pendulum, Fuzzy control, Fuzzy modeling, Description of dynamic behavior
Fuzzy control has a distinctive feature in that it can incorporate experts' control rules using linguistic expressions. The authors have presented various types of fuzzy neural networks (FNNs) called Type I-V. The FNNs can automatically identify the fuzzy rules and tune the membership functions of fuzzy controllers by utilizing the learning capability of neural networks. In particular, the Type IV FNN has a simple structure and can express the identified fuzzy rules linguistically. The authors have also proposed a method to describe the behavior of fuzzy control systems based on the fuzzy models. The method can comprehensively express the dynamic behavior of fuzzy control systems and makes easy to know how to modify the fuzzy controllers. This paper studies an acquisition of fuzzy controller for an inverted pendulum using the Type IV FNNs and presents a new method for describing of the behavior of the fuzzy control system. The new method expresses the dynamic ehavior of the fuzzy control system more clearly by incorporating the change of the output of the controlled object. This new rule-to-rule mapping method enables easy modification of the fuzzy control rules. The experimental results illustrate that the method is effective in designing the fuzzy controllers having good performance.
Cite this article as:
S. Horikawa, M. Yamaguchi, T. Furuhashi, and Y. Uchikawa, “Fuzzy Control for Inverted Pendulum Using Fuzzy Neural Networks,” J. Robot. Mechatron., Vol.7 No.1, pp. 36-44, 1995.
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