## 3 Experimental Results and Analysis
### Experimental Steps and Results Analysis  
A variety of methods are used to compare the forms of experiments, with control accuracy and control time as test indicators, the following experiments are carried out. The detailed steps and experimental results are as follows:  
The experiment is based on the tracking effect of the fuzzy neural network synchronization control algorithm. During the experiment process, the active cylinder of the double-cylinder forging hydraulic press is selected as the measured object. The setting cylinder speed is changed from 3 mm/s to 6 mm/s at 3 s, changed from 6 mm/s to 3 mm/s at 6 s, and changed from 3 mm/s to 0 mm/s at 9 s. The simulation process is shown in Figure 3.  
Figure 2: AMESim and Simulink Co-simulation Flow. In Figure 3, the solid line shows the cylinder tracking speed curve and the dashed line shows the given speed curve. It can be seen from Figure 2 that when the cylinder speed changes from 3 mm/s to 6 mm/s at 3 s, 6 mm/s to 3 mm/s at 6 s, 3 mm/s to 0 at 9 s, the tracking curve accurately completes tracking at 3 s, the second tracking at 6.5 s and the third tracking at 9.2 s. It can be seen that the cylinder can track the given speed in a relatively short time during the whole simulation process. In the case of sudden change of speed signal, the speed change is smooth and there is no impact phenomenon, which ensures the accuracy of control. In order to verify the stability of the control method, the control time of the algorithm is lengthened, and the synchronization control algorithm based on multi-factor self-adjusting fuzzy control is adopted. The improved synchronization control algorithm based on the improved fractional-order non-growth adaptive law is compared with the proposed algorithm. The result is shown in Figure 4.  
Figure 4: \(Cont.\)  
Figure 3: Velocity Mutation Tracking Curve. It can be seen from Figure 4 that the stability of the method has been maintained above 90% with the increase of time. With the increase of time, the stability of the synchronous control algorithm based on multi-factor self-tuning fuzzy control decreased from 50% to 20%. The stability of the synchronous control algorithm based on the improved fractional-order non-growth adaptive law is reduced from 38% to 20%. During the experiment, the double-cylinder forging hydraulic press was synchronously controlled using different algorithms. The results obtained through the experiment are shown in Figure 5.  
Figure 4: Comparison of the stability of different algorithms. (**a**) Synchronous control algorithm based on fuzzy neural network. (**b**) Synchronization control algorithm based on multi-factor self-tuning fuzzy control. (**c**) Synchronous control algorithm based on improved fractional order non-growth adaptive law. Figure 5: _Cont._  
The two curves in Figure 5 show the motion range of the two cylinders of the hydraulic press controlled by different algorithms. By comparing with Figure 5, we can see that the motion amplitude of the synchronous control algorithm based on multi-factor self-adjusting fuzzy control is far from the expected amplitude curve, at about 0.7 mm. The motion amplitude of the synchronous control algorithm based on improved fractional non-growth adaptive law is the furthest from the expected amplitude curve, at about 1.2 mm. After the synchronous control algorithm based on fuzzy neural network is controlled, the liquid phase is controlled by the synchronous control algorithm. The double cylinder motion range of the press is the closest, which is about 0.3 mm outside of the expected range, which ensures the synchronous control effect of the double cylinder forging hydraulic press. The experimental results show that the control accuracy of the proposed algorithm is the highest. The synchronization control algorithm based on fuzzy neural network analyzes the principle of synchronous control of the hydraulic press during the process of meeting the hydraulic press.