Modelling and Novel Neural Network Predictive Control of a Fixed Displacement Motor System Controlled by an Electrohydraulic Proportional Variable Displacement Pump

This paper proposes a neural network predictive control (NNPC) method to address the challenge of controlling the output speed in a fixed displacement motor (FDM) system controlled by an electrohydraulic proportional variable displacement pump (EHP-VDP). The dynamic response of the system is divided into four segments, and mathematical models are derived for each segment. The system is modelled using over 400 input-output data pairs, with 75% of the data used for training and the remaining 25% for validation. The Levenberg-Marquardt algorithm (LMA) is employed to optimise the neural network, achieving optimal validation performance with a mean square error of 0.0029637 after 258 epochs. This real-time optimisation enables the output speed of the system to accurately track the reference speeds. Simulink simulations are conducted over a 25-second duration covering five reference speed intervals, demonstrating reliable tracking performance. The NNPC method shows competitive performance compared with backpropagation-neural network-proportional integral derivative (BP-NN-PID) and proportional integrative derivative (PID) controllers, achieving the root mean square error (RMSE), the mean absolute error (MAE), and the integral squared error (ISE) values of 6.72, 1.00, and 572.89, respectively. These results indicate that the NNPC offers advantages in fast-response scenarios while maintaining acceptable tracking accuracy.