Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems With Reduced Parametric Sensitivity

被引：230

作者：

Precup, Radu-Emil ^{[1
,2
]}

David, Radu-Codrut ^{[1
]}

Petriu, Emil M. ^{[3
]}

机构：

[1] Politehn Univ Timisoara, Dept Automat & Appl Informat, Timisoara 300223, Romania

[2] Edith Cowan Univ, Sch Engn, Joondalup, WA 6027, Australia

[3] Univ Ottawa, Sch Elect Engn & Comp Sci, Ottawa, ON K1N 6N5, Canada

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2017年 / 64卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Experimental results; fuzzy control systems (CSs); Grey Wolf Optimizer (GWO); parametric sensitivity; servo systems; SEARCH ALGORITHM; FAULT-DIAGNOSIS; NEURAL-NETWORKS; PI; MANAGEMENT; STABILITY; PROGNOSIS; SUPPORT; SENSOR;

D O I：

10.1109/TIE.2016.2607698

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs consist of servo system processes controlled by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs). The process models have second-order dynamics with an integral component, variable parameters, a saturation, and dead-zone static nonlinearity. The sensitivity analysis employs output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The GWO algorithm is used in solving the optimization problems, where the objective functions include the output sensitivity functions. GWO's motivation is based on its low-computational cost. The tuning approach is validated in an experimental case study of a position control for a laboratory nonlinear servo system, and TSK PI-FCs with a reduced process small time constant sensitivity are offered.

引用

页码：527 / 534

页数：8