# Research on Control of Intelligent Vehicle Human-Simulated Steering System Based on HSIC
## 2 Polynomial Fitting of Vehicle Driving Trajectory
### The Design of Intelligent Vehicle Human-Simulated Steering Controller  
Intelligent vehicle automatic steering system is a nonlinear time-varying complex system. There are many uncertain factors, which enhances the difficulty of control strategy design. To analyze the operating characteristics of the steering system, the steering process is divided into three stages, namely, the steering start stage, the steering maintenance stage, and the return stage. Each process includes two aspects of angle and torque, namely, the angle and torque change phase, the angle and torque maintenance phase, and the angle and torque return stage. The changes of each stage includes the following modes, such as the position expression of target trajectory in the deviation phase plane \(e\)\(-\)\(\dot{e}\), as shown in Figure 5.  
Figure 5: Steering system (angle and torque) phase plane design.  
When the deviation is large, corresponding to area 1, the largest possible control effect, namely, pound-pound control is adopted.  
When the deviation and the change rate of the deviation are small (satisfying the requirements), corresponding to area 5, in order to eliminate the error, PID control is adopted.  
If the deviation is large, corresponding to area 2 proportional modal control is adopted. In order to ensure the low deviation change speed, weak differential control can be introduced on the basis of proportional mode.  
In the process of reducing the deviation, if the deviation change speed is lower than or equal to the predetermined speed, corresponding to area 4, the proportional mode plus a differential mode control is adopted. In the process of reducing the deviation, if the deviation change speed is greater than the predetermined speed, corresponding to 3, strong differential control is introduced to the proportional mode, so that the deviation change speed is reduced as fast as possible.  
Therefore, according to the deviation and deviation change speed, the operation control level can be divided into five modes:  
Control mode 1: \(u_{n}=\mathrm{sgn}(e_{n})\cdot U_{\mathrm{max}}\), \(|e_{n}|>e_{1}\)  
Control mode 2: \(u_{n}=k_{p2}\cdot e+k_{d2}\cdot\dot{e}\), \(|e_{n}|<e_{1}\cap|e_{n}|>e_{2}\)  
Control mode 3: \(u_{n}=k_{p3}\cdot e+k_{d3}\cdot\dot{e}\), \(|e_{n}|<e_{2}\cap|\dot{e}_{n}|\geq\left|\dot{e}_{1}\right|\)  
Control mode 4: \(u_{n}=k_{p4}\cdot e+k_{d4}\cdot\dot{e}\), \(|e_{n}|<e_{2}\cap|\dot{e}_{n}|<|\dot{e}_{1}|\cap\
eg\big{(}|e_{n}|<e_{3}\cap| \dot{e}_{e}|<|\dot{e}_{2}|\big{)}\)  
Control mode 5: \(u_{n}=k_{p5}\cdot e+k_{d5}\cdot\dot{e}+k_{i5}\!\!\int edt\), \(|e_{n}|<e_{3}\cap|\dot{e}_{n}|<|\dot{e}_{2}|\)  
where \(e\) is the system deviation, \(\dot{e}\) is the rate of deviation change, \(u_{n}\) is the output of the controller, \(U_{\mathrm{max}}\) is the maximum control output, and \(e_{n}\) is the \(n\)-th peak of the deviation.  
According to the above analysis, the human-simulated control system of the steering system is established in the MTLAB/Simulink environment, which mainly includes five control modes, deviation differential links, time-delay links, and controlled object design, as shown in Figure 6.  
The overall system control model based on Simulink/Carsim joint simulation is established, as shown in Figure 7.  
Figure 6: Steering system human-simulated control system based on MTLAB/Simulink.