Application of de strategy and neural network - in position control of a flexible servohydraulic system

One of the most promising novel evolutionary algorithms is the Differential Evolution (DE) algorithm for solving global optimization problems with continuous parameters. In this article the Differential Evolution algorithm is proposed for handling nonlinear constraint functions to find the best initial weights of neural networks. The highly non-linear behaviour of servo-hydraulic systems makes them idea subjects for applying different types of sophisticated controllers. The aim of this paper is position control of a flexible servo-hydraulic system by using back propagation algorithm. The poor performance of initial training of back propagation motivated to apply the DE algorithm to find the initial weights with global minimum. This study is concerned with a second order model reference adaptive position control of a servo-hydraulic system using two artificial neural networks. One neural network as an acceleration feedback and another one as a gain scheduling of a proportional controller are proposed. The results suggest that if the numbers of hidden layers and neurons as well as the initial weights of neural networks are chosen well, they improve all performance evaluation criteria in hydraulic systems.