DRNN neural network control on electro-hydraulic load simulator

A new control structure for load simulator to improve its performance is presented, in which the DRNN neural network is adopted to combine with PID controller. The DRNN is able to deal with dynamic problem and to identify the broad sense inverse model. It includes system input, output and disturbance. The nonlinear problem will be solved properly. The neural network PID controller is designed to overcome the shortcoming of the only neural network controller. The speed is taken as reference input of the DRNN neural network, which improves the system's robustness to the influence of extraneous force. The simulation and experiment are carried out, which show a good result. This method is of validity and robustness.