期刊
AUTOMATION IN CONSTRUCTION
卷 23, 期 -, 页码 55-63出版社
ELSEVIER
DOI: 10.1016/j.autcon.2011.12.005
关键词
Hybrid evolutionary algorithm; Recurrent neural network; Tower crane; Underactuated system; Nonlinear system control
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.
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