Flow Rate Control of Variable Displacement Piston Pump with Pressure Compensation Using Neural Network

One of the most important components in hydraulic circuits is the pump that generates hydraulic power supply. The performance and the versatility of a variable displacement piston pump are, to a large extent, determined by its controller. Upgrading of existing controllers is considered one of the major means of improving the characteristics of the pump. The dynamic performance of hydraulic systems with a demand flow supply not only depends on the performance of the flow modulation valve, but also on the performance of the pump. In this study, the performance of soft computing methodology, trained Neural Network (NN) based on the conventional PID controller, is used for the control of a swash plate displacement while the compensation of the effect of the back up pressure is implemented by inverse NN model. The feasibility of system is simulated and issue of implementation such pumps control is established. It is seen that the use of the proposed methodology results in some desirable characteristics.