## 5 Simulation Experiment of pH Control of Fertilizer Solution  
In order to verify the performance of the composite control scheme, the Simulink simulation interface of the fuzzy controller was first designed through MATLAB, and then the BP-PID control algorithm based on PSO optimization was written using the S-function module of the Simulink module. PID control, fuzzy control, BPNN-PID control with fuzzy preprocessing and BPNN-PID control system models based on improved PSO and fuzzy preprocessing were established in MATLAB/Simulink, as shown in Figure 11. The workflow of the model running is shown in Figure 12. The pH value of pure water was 8, the acid used to adjust pH was dilute hydrochloric acid with a concentration of 0.2 mol/L, the flow rate of water entering the mixing tank was 1.1 L/s and the system delay time was 4 s.  
Figure 10: IPSO-Fuzzy-BPNN-PID system schematic diagram. The pH value of the fertilizer solution was tracked with a simulation time of 1000 s; the tracking curves of different control algorithms are shown in Figure 13. According to Figure 13, it was found that the PID controller has a fast response but a large overshoot, the fuzzy control has a small overshoot but a slow rise time, the fuzzy neural network controller has a faster response time but there is still a certain amount of overshoot, and the IPSO-Fuzzy-BPNN-PID controller has a faster response time with a small overshoot and can respond to changes in the set pH value in a short time. Compared with the other three control algorithms, it is more conducive to improving the accuracy of the fertilization process. In theory, this composite controller could optimize the pH regulation of fertilizers and water. In order to further analyze the optimization performance of the controller in this paper, the process of adjusting the pH value of the water and fertilizer mixture from 7.5 to 6.5 was simulated according to the actual situation. The simulation time was 500 s. The control curves of several control algorithms are shown in Figure 14. According to Figure 14, the fuzzy control and PID control have the largest response time and overshoot, respectively, and the fuzzy neural network response is faster, but there is a certain amount of overshoot. The controller has a faster response time but there is still a certain amount of overshoot. of overshoot over a period of time. The performance parameters of the four algorithms are shown in Table 2. From Table 2, it was found that in terms of overshoot amount, it is reduced by 7.0, 2.0 and 1.7 percentage points compared to the first three algorithms. The steady times are reduced by 60 s, 235 s and 88 s. The IPSO-Fuzzy-BPNN-PID controller performed well in terms of overshoot and response time, and was able to reach the set pH value in a short time. In the next phase, the algorithm is tested. The test system structure mainly consisted of a main control module, a signal transmission module, a sensor monitoring module and a drive module. The main control module was mainly composed of a computer, PLC controller and touch screen configuration interface. The signals were transmitted via an RS485 bus. The drive control module was divided into the fertilizer-proportioning drive module and irrigation control drive module. The tests took the set pH value as input value and used PLC as the control core to receive the actual feedback signal from the pH sensor and perform calculations. When blending the fertilizer, the system master control module controlled the fertilizer output frequency of the fertilizer-proportioning drive module by changing the analogue voltage signal, controlled the input volume of the regulating liquid as it was added to the mixing tank according to the calculated volume and completed the mixing, and the system maintained a stable state when the pH reached the set value.