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Mathematical Problems in Engineering
Volume 2010, Article ID 124014, 16 pages
Research Article

Modelling of the Automatic Depth Control Electrohydraulic System Using RBF Neural Network and Genetic Algorithm

1School of Mechanical Engineering, Nanjing University of Science & Technology, Jiangsu 210094, China
2State Key Laboratory of Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

Received 1 February 2010; Revised 30 June 2010; Accepted 4 November 2010

Academic Editor: Tamas Kalmar-Nagy

Copyright © 2010 Xing Zong-yi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The automatic depth control electrohydraulic system of a certain minesweeping tank is complex nonlinear system, and it is difficult for the linear model obtained by first principle method to represent the intrinsic nonlinear characteristics of such complex system. This paper proposes an approach to construct accurate model of the electrohydraulic system with RBF neural network trained by genetic algorithm-based technique. In order to improve accuracy of the designed model, a genetic algorithm is used to optimize centers of RBF neural network. The maximum distance measure is adopted to determine widths of radial basis functions, and the least square method is utilized to calculate weights of RBF neural network; thus, computational burden of the proposed technique is relieved. The proposed technique is applied to the modelling of the electrohydraulic system, and the results clearly indicate that the obtained RBF neural network can emulate the complex dynamic characteristics of the electrohydraulic system satisfactorily. The comparison results also show that the proposed algorithm performs better than the traditional clustering-based method.