Modeling, stability analysis, and computational aspects of some simplest nonlinear fuzzy two-term controllers derived via center of area/gravity defuzzification

被引:32
作者
Arun, N. K. [1 ]
Mohan, B. M. [1 ]
机构
[1] Indian Inst Technol, Dept Elect Engn, Kharagpur 721302, W Bengal, India
关键词
Center of gravity/area defuzzification; Mathematical modeling; Stability analysis; Computational aspects; PI/PD control; Nonlinear control; Fuzzy control; Mamdani type controller; PD-TYPE; PI; DESIGN; INPUT; GAIN;
D O I
10.1016/j.isatra.2017.04.023
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The mathematical models reported in the literature so far have been found using Center of Sums (CoS) defuzzification method only. It appears that no one has found models using Center of Area (CoA) or Center of Gravity (CoG) defuzzification method. Although there have been some works reported to deal with modeling of fuzzy controllers via Centroid method, all of them have in fact used CoS method only. In this paper, for the first time mathematical models of the simplest Mamdani type fuzzy Proportional Integral (PI)/Proportional Derivative (PD) controllers via CoG defuzzification are presented. L-type and Gamma-type membership functions over different Universes of Discourse (UoDs) are considered for the input variables. L-type, Pi-type and Gamma-type membership functions are considered for the output variable. Three linear fuzzy control rules relating all four input fuzzy sets to three output fuzzy sets are chosen. Two triangular norms namely Algebraic Product (AP) and Minimum (Min), Maximum (Max) triangular co-norm, and two inference methods, Larsen Product (LP) and Mamdani Minimum (MM), are used. Properties of the models are studied. Stability analysis of closed-loop systems containing one of these controller models in the loop is done using the Small Gain theorem. Since digital controllers are implemented using digital processors, computational and memory requirements of these fuzzy controllers and conventional (nonfuzzy) controllers are compared. A rough estimate of the computational time taken by the digital computer while implementing any of these discrete-time fuzzy controllers is given. Two nonlinear plants are considered to show the superiority of the simplest fuzzy controller obtained using CoA or CoG defuzzification method over the simplest fuzzy controller obtained using CoS method and reported recently. Real-time implementation of one of the developed controller models is done on coupled tank experimental setup to show the feasibility of the developed model. (C) 2017 ISA. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:16 / 29
页数:14
相关论文
共 52 条
[1]  
Amin Haj-Ali, 2003, International Journal of Fuzzy Systems, V5, P60
[2]  
[Anonymous], INT J COMPUTATIONAL
[3]  
[Anonymous], 1993, INTRO FUZZY CONTROL, DOI DOI 10.1007/978-3-662-11131-4
[4]  
[Anonymous], 2003, 557 QUANS
[5]  
Arun NK, 2014, IFAC P, V47, P882
[6]   Analysis of one dimensional and two dimensional fuzzy controllers [J].
Ban, Xiaojun ;
Gao, Xiaozhi ;
Huang, Xianlin ;
Wu, Tianbao .
Journal of Systems Engineering and Electronics, 2006, 17 (02) :362-373
[7]   A fuzzy logic based PI autotuner [J].
Bandyopadhyay, R ;
Patranabis, D .
ISA TRANSACTIONS, 1998, 37 (03) :227-235
[8]  
Bosukonda M, 2011, MODEL SIMUL ENG, V2011, P32
[9]   Tuning fuzzy PD and PI controllers using reinforcement learning [J].
Boubertakh, Hamid ;
Tadjine, Mohamed ;
Glorennec, Pierre-Yves ;
Labiod, Salim .
ISA TRANSACTIONS, 2010, 49 (04) :543-551
[10]   A PD-like self-tuning fuzzy controller without steady-state error [J].
Chao, CT ;
Teng, CC .
FUZZY SETS AND SYSTEMS, 1997, 87 (02) :141-154