Fuzzy Inference System Approach Using Clustering and Differential Evolution Optimization Applied to Identification of a Twin Rotor System

被引：6
作者：
Coelho, Leandro dos Santos [1 ,2 ]
Pessoa, Marcelo Wicthoff [2 ]
Mariani, Viviana Cocco [1 ,3 ]
Reynoso-Meza, Gilberto [1 ]
机构：
[1] Pontifical Catholic Univ Parana PUCPR, Ind & Syst Engn Grad Program PPGEPS, Curitiba, Parana, Brazil
[2] Fed Univ Parana UFPR, Elect Engn Grad Program PPGEE, Curitiba, Parana, Brazil
[3] Pontifical Catholic Univ Parana PUCPR, Mech Engn Grad Program PPGEM, Curitiba, Parana, Brazil
来源：
IFAC PAPERSONLINE | 2017年 / 50卷 / 01期
关键词：
Nonlinear identification; fuzzy system; differential evolution; evolutionary computation;
D O I：
10.1016/j.ifacol.2017.08.2162
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a Takagi-Sugeno-Kang (TSK) fuzzy inference system using fuzzy c-means clustering and differential evolution optimization is proposed and validated when applied to a twin rotor system (TRS). The TRS is perceived as a challenging problem due to its strong cross coupling between horizontal and vertical axes. The design procedure of the TSK fuzzy approach for TRS is detailed. According to the identification results obtained by applying the TSK fuzzy approach and a nonlinear autoregressive with moving average and exogenous inputs (NARMAX) model, the effectiveness of the proposed fuzzy system design is demonstrated through validation tests. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：13102 / 13107
页数：6
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