Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

被引：92

作者：

Reynoso-Meza, Gilberto ^{[1
]}

Blasco, Xavier ^{[1
]}

Sanchis, Javier ^{[1
]}

Martinez, Miguel ^{[1
]}

机构：

[1] Univ Politecn Valencia, Inst Univ Automat & Informat Ind, Valencia 46022, Spain

来源：

CONTROL ENGINEERING PRACTICE | 2014年 / 28卷

关键词：

Evolutionary multi-objective optimisation; Multi-objective evolutionary algorithms; Multi-criteria decision making; Multi-objective optimisation design procedure; Controller tuning; PID CONTROLLERS; DIFFERENTIAL EVOLUTION; GENETIC ALGORITHM; DECISION-MAKING; DESIGN OPTIMIZATION; ENGINEERING DESIGN; PREDICTIVE CONTROL; CONTROL-SYSTEMS; FUZZY CONTROL; MODEL;

D O I：

10.1016/j.conengprac.2014.03.003

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach for calculating a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective and search for a set of potentially preferable solutions; the designer may then analyse the trade-offs among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are discussed. Throughout this paper it is noticeable that EMO research has been developing towards different optimisation statements, but these statements are not commonly used in controller tuning. Gaps between EMO research and EMO applications on controller tuning are therefore detected and suggested as potential trends for research. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：58 / 73

页数：16

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