Neural Network Based on Direct Inverse Control for Electro-Hydraulic Servo Drive

This paper describes the use of the nonlinear autoregressive with exogenous inputs neural network for the control of electro−hydraulic servo drive. The direct inverse controller was trained on a real object in an online way with the use of a programmable logic controller before starting work on a real object, and appropriate tests were performed in the MATLAB/Simulink environment in order to select the right structure of the neural network. Also, the paper includes various network learning algorithms: gradient descent, resilient backpropagation, and adaptive moment estimation, which belong to backpropagation algorithms. In order to compare the suitability of the direct inverse controller, the controller was implemented on a real electro−hudraulic test stand, and its performance was compared with the performance of a proportional−integral−derivative controller.