# Research on Active Anti-Slip Control of High-Speed Trains Based on High-Order Sliding Mode
## 4 Experiment Results  
To closely integrate the adhesion control algorithm with practical applications, the algorithm is implemented on the DSP controller, and the train model is downloaded to the ModelingTech semi-physical simulation platform using STAR-SIM software, whose version number is 4.6.2.9. This setup creates a DSP-ModelingTech Hardware-in-the-Loop experimental platform to validate the effectiveness of the proposed algorithm. It is important to note that the output from the semi-physical platform, ModelingTech, contains a certain level of noise. For a visual representation of the experimental process, please refer to Figure 5.  
The train used in this study is an eight-car high-speed train with a 5M3T configuration, where five of the cars are equipped with powered axles, totaling ten moving axles. For a visual reference of the actual train, please refer to Figure 6. The CCS software version used in related simulations or operations is 8.1.0.00011.The simulation parameters for the high-speed train body model are detailed in Table 1. The motor parameters are sourced from [17], and the air braking parameters are provided in [18]. Figure 5: Experimental flowchart. The train operates under different road conditions as follows: from 0 to 115 s, the train runs on dry road conditions; from 115 to 120 s, on icy road conditions; from 120 to 170 s, on icy road conditions; from 170 to 320 s, on wet road conditions; and from 320 to 533 s, on very wet road conditions. The output of the train controller under snowy and icy conditions exceeds the maximum traction force that the current track surface can provide. Without corrective action, the wheelset may begin to slip. The operating conditions of the train are shown in Table 2. The relevant parameters of the O-Polach model for each simulated road condition are presented in Table 3.  
To evaluate the performance of the proposed control method, we conducted experiments and compared it with common control techniques, such as Model Predictive Control (MPC), Robust Control, and PID control. The key metrics include tracking accuracy (RMSE and maximum error), computational efficiency (execution time), and control response time. These metrics are essential for assessing the real-time feasibility and accuracy of the control \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parametric** & **Numerical Value** & **Parameter** & **Value** \\ \hline \(M\) & 500,000 kg & \(g\) & 9.8 m/s2 \\ \(J\) & 93.75 kg\(\cdot\)m2 & \(R_{g}\) & 2.5 \\ \(r\) & 0.445 m & \(B_{t}\) & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Train parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time (s)** & **Surface** & **Perturbation** & **Traction** & **Slippage** \\ \hline
0–115 & Dry & Higher & Higher & None \\
115–120 & Icy/Snowy & Minimal & Minimal & High \\
120–170 & Oily & Low & Low & Moderate \\
170–320 & Wet & Moderate & Moderate & Low \\
320–533 & Damp & Higher & None & None \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Train operating conditions. Figure 6: Actual picture. algorithm. For a detailed comparison of the performance based on key metrics, please refer to Table 4.  
In addition to traditional sliding mode control, adaptive sliding mode control based on disturbance compensation is implemented on the Hardware-in-the-Loop platform to compare the control effects. The specific parameters for each controller are detailed in Table 5.  
The simulation results are shown in Figures 7-10. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control** & **RMSE** & **Max Error** & **Execution** & **Control** \\
**Method** & **(Units)** & **(Units)** & **Time (s)** & **Response** \\ \hline Proposed & 0.02 & 0.10 & 0.005 & 5 \\ Method & 0.05 & 0.20 & 0.030 & 30 \\ Robust Control & 0.04 & 0.18 & 0.020 & 25 \\ PID Control & 0.07 & 0.25 & 0.010 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of control methods in low-adhesion train control. Figure 7: Speed tracking effect comparison chart.