Neural networks modeling of electrohydraulic systems

Aiming at the problem that construction of an accurate model of an electrohydraulic system based on traditional linear methods remains a difficult task due to its nonlinear characteristics including flow/pressure relation, etc, the paper presents a thorough study on modeling of electrohydraulic systems using different types of neural networks. The two widely used neural networks, i.e. the multilayer perceptron neural network (MLPNN) and the radial basis function neural network (RBFNN) were investigated, and three MLPNNs and two RBFNNs were constructed using their five typical training algorithms. All these techniques were then applied to an automatic depth control electrohydraulic system, and the modeling performance of the five networks in the electrohydraulic system was analyzed. The results clearly indicated that the radial basis function neural network with the orthogonal least square training algorithm is prior to other neural networks for modeling of electrohydraulic systems.