Evolutionary algorithms in control systems engineering: a survey

被引：369

作者：

Fleming, PJ ^{[1
]}

Purshouse, RC ^{[1
]}

机构：

[1] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England

来源：

CONTROL ENGINEERING PRACTICE | 2002年 / 10卷 / 11期

关键词：

control system design; genetic algorithms; identification; multiobjective optimisation; on-line control;

D O I：

10.1016/S0967-0661(02)00081-3

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Challenging optimisation problems, which elude acceptable solution via conventional methods, arise regularly in control systems engineering. Evolutionary algorithms (EAs) permit flexible representation of decision variables and performance evaluation and are robust to difficult search environments, leading to their widespread uptake in the control community. Significant applications are discussed in parameter and structure optimisation for controller design and model identification, in addition to fault diagnosis, reliable systems, robustness analysis, and robot control. Hybrid neural and fuzzy control schemes are also, described. The important role of EAs in multiobjective optimisation is highlighted. Evolutionary advances in adaptive control and multidisciplinary design are predicted. (C) 2002 Published by Elsevier Science Ltd.

引用

页码：1223 / 1241

页数：19

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