A hybrid evolutionary algorithm for recurrent neural network control of a three-dimensional tower crane

被引：66

作者：

Duong, Sam Chau ^{[2
]}

Uezato, Eiho ^{[1
]}

Kinjo, Hiroshi ^{[1
]}

Yamamoto, Tetsuhiko ^{[3
]}

机构：

[1] Univ Ryukyus, Fac Engn, Nishihara, Okinawa 9030213, Japan

[2] Univ Ryukyus, Grad Sch Engn & Sci, Nishihara, Okinawa 9030213, Japan

[3] Tokushima Coll Technol, Tokushima, Japan

来源：

AUTOMATION IN CONSTRUCTION | 2012年 / 23卷

关键词：

Hybrid evolutionary algorithm; Recurrent neural network; Tower crane; Underactuated system; Nonlinear system control; PARTICLE SWARM OPTIMIZATION; GENETIC ALGORITHM; STABILITY;

D O I：

10.1016/j.autcon.2011.12.005

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

This paper is concerned with the control of an underactuated three-dimensional tower crane system using a recurrent neural network (RNN) which is evolved by an evolutionary algorithm. In order to improve the performance in evolving the RNN, a hybrid evolutionary algorithm (HEA) which utilizes the operators of a constricted particle swarm optimization (PSO) and a binary-coded genetic algorithm (GA) is proposed. Simulation results show that the proposed HEA has superior performance in a comparison with the canonical algorithms and that the control system works effectively. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：55 / 63

页数：9

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