Application of ANFIS in the design of fuzzy controller

Author

Teng-Fei Li ; Chao-Ying Liu ; Zhe-Ying Song ; Xue-Ling Song ; Qing-qing Yan

Author_Institution

Coll. of Electr. Eng. & Informational Sci., Hebei Univ. of Sci. & Technol., Shijiazhuang, China

Volume

3

fYear

2012

fDate

15-17 July 2012

Firstpage

825

Lastpage

829

Abstract

How to generate and adjust the membership function and fuzzy rules are difficult problems in the design of fuzzy controller. To solve this problem, adaptive neural fuzzy inference system (ANFIS) is used to design the fuzzy control system, then the fuzzy rules and membership function can be obtained by back propagation or hybrid algorithm of the neural network. In order to verify the validity of the method, the distillation column temperature control system and CFBB temperature control system are simulated respectively. The results of simulation show that the membership functions and the fuzzy rules of the fuzzy controller can be obtained by the training of the PID control´s input/output data, and it can convert PID control experiences into fuzzy control rules efficiently.

Keywords

adaptive control; backpropagation; control system synthesis; distillation equipment; fuzzy control; fuzzy neural nets; neurocontrollers; temperature control; ANFIS; CFBB temperature control system simulation; adaptive neural fuzzy inference system; back propagation; distillation column temperature control system simulation; fuzzy controller design; fuzzy rule; hybrid algorithm; membership function; Abstracts; Indium phosphide; Niobium; Adaptive neural fuzzy inference system (ANFIS); Fuzzy control; Temperature control;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2012 International Conference on

Conference_Location

Xian

ISSN

2160-133X

Print_ISBN

978-1-4673-1484-8

Type

conf

DOI

10.1109/ICMLC.2012.6359459

Filename

6359459