## 6 Simulation Experimental Results
### Controller Performance Comparison  
As we all know, the PID control algorithm is widely used in train operation control, such as the Beijing Metro Yizhuang Line. In order to prove the advantages of the RBFNN-POCRAC controller in terms of control accuracy and anti-interference, this paper chose position output-constrained robust adaptive control (POCRAC), PID control, and RBFNN-PID control in the literature [30] as the comparison objects, loaded the same desired tracking curve shown in Figure 3, and compared the tracking error of the four controllers.  
It can be seen from Figure 9 that the RBFNN-POCRAC controller converged quickly to the whole process of desired position tracking in the face of strong additional resistance and external interference. When the train ran to 158 s, the position error reached the maximum value of 0.142 m, and the position tracking error at other times was almost zero, showing strong anti-interference ability. When the POCRAC controller faced external interference, the position error convergence was slow, and the maximum position error was 0.2 m. PID control and RBFNN-PID control exhibited a large position error throughout the process of train operation, being far greater than the 0.2 m position error defined in this article. The position error of PID control fluctuated greatly, and the control performance was poor. Compared with PID, RBFNN-PID control effectively reduced the position error of train operation control; however, under the condition of uncertain model parameters and external interference, the position error was significantly larger than the controller designed in this paper.  
It can be seen from Figure 10 that when the four controllers faced strong additional resistance and external interference, the train running speed error of the RBFNN-POCRAC controller was the smallest, and the train running speed error and fluctuation of the PID controller were the largest. Compared with the PID control method, RBFNN-PID control obviously alleviated the speed error, but it was still greater than the speed tracking error of the POCRAC controller and RBFNN-POCRAC controller. The POCRAC controller lacked the ability to adapt to the strong additional resistance and external interference, leading to a certain overshoot of the operation speed, and the control performance was not ideal.  
Figure 9: Position error comparison.  
It can be seen from Figures 9 and 10 that the RBFNN-POCRAC controller had the fastest convergence speed and the smallest overall position error and speed error in the face of the uncertain model parameters, strong additional resistance, and external interference, and it had good control performance and strong adaptive ability, which could ensure the safe and reliable operation of the train, has along with universal engineering practicability.  
In order to further illustrate the control effect of the RBFNN-POCRAC controller, this paper changed the train model parameters of the four controllers within the range of \(\pm 10\%\) and performed 100 simulation runs. The comparison results of the position error range, speed error range, and average parking accuracy are shown in Table 2. It can be seen that the RBFNN-POCRAC controller had the best position and speed tracking performance and the highest parking accuracy, and it could adapt to the external environment of train operation and the changes in train dynamics model parameters, thus meeting the requirements of FAO's high-precision operation control.