# Research on Control of Intelligent Vehicle Human-Simulated Steering System Based on HSIC
## 2 Polynomial Fitting of Vehicle Driving Trajectory
### The Performance Simulation Analysis of Human-Simulated Steering Control  
The vehicle model parameters are shown in Table 3.  
For the accurate acquisition of human-simulated intelligent control parameters, there is no uniform and effective method. In this paper, the tested and tried method is used to obtain the control parameters. By comparing the system control performance under different sequence control parameters, a group with relatively good control performance is selected. The parameters are used as the actual control parameters of the system. The relevant parameters of the controller are set as follows: \(U_{\text{max}}=5\), \(k_{p2}=5\), \(k_{d2}=2\), \(k_{p3}=5\), \(k_{d3}=3\), \(k_{p4}=10\), \(k_{d4}=0.8\), \(k_{p5}=50\), \(k_{d5}=9\). According to Figure 8, under normal right-steering condition, the steering wheel angle and torque obtained by human-simulated intelligent control, whether at a lower vehicle speed (20 km/h) or a higher vehicle speed (50 km/h), can better follow the steering wheel angle and torque of the reference trajectory and have good control effect. The traditional PID control algorithm can effectively follow the reference curve in the initial stage. However, with the increase of the steering wheel angle, obvious control lag and control overshoot occur in PID control. Therefore, the human-simulated intelligent control algorithm has much better control effect than the traditional PID control algorithm. In addition, the yaw rate of the human-simulated intelligent control is significantly lower than that of the traditional PID, indicating that the human-simulated intelligent control can effectively improve the comfort of passengers. It can be found from the low lateral deviation that the vehicle is always in a stable state. Therefore, the proposed control method can effectively improve the comfort of the passengers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Numerical Value** & **Unit** \\ \hline Vehicle mass & 1558 & kg \\ Length/width/high & 4915/1845/1470 & mm \\ Wheelbase & 2775 & mm \\ Front wheel distance/rear wheel & 1585/1580 & mm \\ distance & 1110/1665 & mm \\ Moment of inertia of the vehicle & 671.3/1972.8/2315.3 & kg\(\cdot\)m\({}^{2}\) \\ around the X, Y, Z-axis & 225/50 R17 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Vehicle model parameters. Figure 7: Overall control system based on Simulink/Carsim. According to Figure 9, whether at the lower speed (20 km/h) or the higher speed (30 km/h), the steering wheel angle and torque obtained by the human-simulated intelligent control can better follow steering wheel angle and torque under the U-turn condition. The traditional PID control algorithm can effectively follow the reference curve in the initial stage. However, with the increase of the steering wheel angle, the PID control shows slightly control lag and control overshoot. This is because during the normal right-steering condition, the turning radius is relatively large, and the turning is only once. However, in the process of U-turn condition, the vehicle speed is slower, the turning radius is smaller, and the turning occurs two times. When the vehicle speed is low, the PID control effect will be better, so the PID lag in the U-turn condition compared  
Figure 8: Comparison and analysis of control results under normal right-steering condition: (**a**) 20 km/h, comparison of steering wheel angle; (**b**) 20 km/h, comparison of steering wheel torque; (**c**) 20 km/h, comparison of yaw rate; (**d**) 20 km/h, comparison of lateral deviation; (**e**) 50 km/h, comparison of steering wheel angle; (**f**) 50 km/h, comparison of steering wheel torque. with the normal right-steering condition is not obvious. Therefore, the control effect based on human-simulated intelligent control calculation is better than the traditional PID control algorithm at imitating experienced drivers.