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.

The paper presents an approach to model nonlinear dynamic behaviors of the Automatic Depth Control Electrohydraulic System (ADCES) of a certain minesweeping weapon with Radial Basis Function (RBF) neural networks trained by hierarchical genetic algorithm. In the proposed hierarchical genetic algorithm, the control genes are used to determine the number of hidden units, and the parameter genes are used to identify center parameters of hidden units. In order to speed up convergence of the proposed algorithm, width and weight parameters of RBF neural network are calculated by linear algebra methods. The proposed approach is applied to the modelling of the ADCES, and experimental results clearly indicate that the obtained RBF neural network can emulate complex dynamic characteristics of the ADCES satisfactorily. The comparison results also show that the proposed approach performs better than the traditional clustering-based method.

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The electrohydraulic servo system of a certain type of mines weeping plough is a complex and nonlinear system. It is difficult to construct its accurate model by first principle method and to achieve satisfactory control performance by traditional PID controller. In this paper, the radial basis function neural network with orthogonal least square learning algorithm is used to model the electrohydraulic system and the neural network based direct inverse is adopted to control the system. The experimental results and comparisons with other techniques clearly show the validity of the proposed methods.