Abstract: Abstract: Flow rate and pressure are two important control parameters of the irrigation system and they determine if the irrigation process can meet the irrigation demand or not. In the same pipeline system, flow rate and pressure affect each other, and it is difficult to control them separately. This study explored the relationship between flow rate and pressure and their control parameters, established a control technology suitable for the head of water distribution of the multi-user irrigation system. By analyzing the water supply characteristic curve of the pipeline water supply system, a flow pressure coupling adjustment method was proposed. This method allowed system controller output voltage analog and current analog to control the frequency of the inverter and the valve opening of the electric valve. At the same time, a proportion integration differentiation (PID) controller was used to realize the coupling control of the flow rate and the pressure. In addition, a method that use generalized regression neural network (GRNN) to accelerate the speed of PID (GRNN_PID) was proposed in order to speed up the response of irrigation control system, improve the operating efficiency and ensure the safety of the system. The flow, pressure and corresponding control quantities data were obtained through experiments, and the GRNN method was used to fit the relationship between them. Afterwards, the control quantities required for the target flow and pressure were quickly calculated by the fitting model and were used directly to control the corresponding actuator (pump and electric valve) so as to fine-tune the flow and pressure through PID. The GRNN training results showed that the relative error of the analog quantity used to control the frequency of the frequency converter was between 0.11% and 3.86%, and the relative error of the analog quantity used to control the electric valve was between 0.09% and 5.74%, indicating that the GRNN model has a high fitting accuracy. Three adjustment processes was used to simulate the water demand behavior of three users to verify the control model, and the tests showed that the adjustment times of the three processes of the GRNN_PID model were 11.6, 10.7 and 7.2 s, respectively, and the adjustment times of the three processes of the PID model were 31.7, 29.6 and 16.9 s, respectively. The GRNN_PID model greatly reduced the adjustment time of the system and improved the operating efficiency of the system. The control accuracies of GRNN_PID and traditional PID adjustment method were compared, and the results showed that the steady-state error of the GRNN_PID adjustment method and the traditional PID adjustment method were both within 1%, and the maximum overshoot was below 8%, which means that the control accuracy is high but the difference is not very big. The reason that leads to this result above is that GRNN accelerates the system regulation speed from the strategy, and it does not change the parameters of PID, and thus it has little impact on the control accuracy of the system. This research can provide tools for the rapid control of flow and pressure in irrigation system.