Online modeling and prediction of a hydraulic force-acting system using neural networks

Investigates the experimental modeling of the dynamic behavior of a force-acting industrial hydraulic actuator using a neural network (NN). Due to variable environmental stiffness as well as the characteristics of hydraulic components, the dynamics of the system is time-varying and highly nonlinear. It is therefore desirable to develop a nonlinear modeling scheme, based on NNs, to estimate and predict the output of the system online. In this paper, the predictability of an online-trained NN modeling a hydraulic force-acting system is first compared to a linear model. The result demonstrates that the NN outperforms its linear counterpart in terms of multi-step prediction. Then, a more detailed discussion of the online training of the NN is provided. The related aspects include the choice of the window length, the NN´s structure and the criterion for terminating the training. The work studied in this paper should help in the design of appropriate force-control law and/or fault diagnosis algorithms