Modeling and prediction of hydraulic servo actuators with neural networks

Presents a neural network (NN) based methodology for modeling and prediction of hydraulic servo actuators performance, via experimental data. The predictability of a trained NN to modeling a hydraulic actuator is first compared to a linear model. The result demonstrates the excellent ability of the NN in terms of multi-step prediction. Next, a state-space model using neural networks (NNs) to approximate the multivariable states (i.e., displacement, velocity and line pressures) of the hydraulic actuator is developed. The training algorithm and the criterion for the measurement of output errors are discussed. Test results show that NNs are capable of modelling and predicting the highly nonlinear hydraulic actuator even in a noisy environment