Automatic T-S fuzzy model with application to designing predictive controller

Zhi-gang Su1, Pei-hong Wang1 and Yu-fei Zhang1

  1. School of energy & environment, Southeast University
    Nanjing, Jiangsu 210096, China
    Zhigangsu@seu.edu.cn

Abstract

A novel methodology is proposed to automatically extract T-S fuzzy model with enhanced performance using VABC-FCM algorithm, a novel Variable string length Artificial Bee Colony algorithm (VABC) based Fuzzy C-Mean clustering technique. Such automatic methodology not requires a priori specification of the rule number and has low approximation error and high prediction accuracy with appreciate rule number. Afterward, a new predictive controller is then proposed by using the automatic T-S fuzzy model as the dynamic predictive model and VABC as the rolling optimizer. Some experiments were conducted on the superheated steam temperature in power plant to validate the performance of the proposed predictive controller. It suggests that the proposed controller has powerful performance and outperforms some other popular controllers.

Key words

T-S fuzzy model, fuzzy c-means; automatic; Artificial Bee Colony; predictive control, superheated steam temperature

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS120222059S

Publication information

Volume 9, Issue 4 (December 2012)
Special Issue on Recent Advances in Systems and Informatics
Year of Publication: 2012
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Su, Z., Wang, P., Zhang, Y.: Automatic T-S fuzzy model with application to designing predictive controller. Computer Science and Information Systems, Vol. 9, No. 4, 1577-1602. (2012)