## 1 Introduction  
With the increasing maturity of control technology and the continuous improvement of drivers' requirements for safety, maneuverability and ride comfort, the intelligent research of vehicles has received extensive attention [1]. Intelligent vehicles have better road utilization and higher safety, and their related research has been one of the research hotspots in recent years. Path-tracking control is a relatively common intelligent driving scheme in the intelligent driving process of intelligent vehicles. It aims to ensure that the heading error and lateral error in trajectory tracking approach zero. There have been many research results. [PERSON] et al. [2] considered the longitudinal, lateral, yaw, and quasi-static roll motion of intelligent vehicles, designed a trajectory tracking control system based on model predictive control, and proposed a vehicle active and safe steering control method. In [3], a tracking-error learning control (TELC) algorithm was presented. In the TELC algorithm, the feedforward control actions are updated by using the tracking error dynamics, and the plant-model mismatch problem is thus discarded. Therefore, the feedforward controller gradually eliminates the feedback controller from the control of the system once the mobile robot has been put on-track. In addition to the proof of the stability, it was proven that the cost functions do not have local minima, such that the coefficients in the TELC algorithm guarantee that the global minimum is reached. The experimental results show that the TELC algorithm results in better path-tracking performance than the traditional tracking error-based control method. In [4], a path-tracking method based on model predictive control with a variable predictive horizon is proposed. Based on the designed model's predictive controller for path tracking, the response analysis of the path-tracking control system under the different predictive horizons was carried out to clarify the influence of predictive horizon on path tracking accuracy, driving comfort and real-time of the control algorithm. In [5], researchers obtained the preview lateral position error according to the car motion parameters, road curvature and preview error model. They used this error to design a trajectory tracking control strategy to solve the front wheel angle that needs to be controlled when the car reaches the preview point in order to achieve the goal of tracking the desired trajectory. In [6], a potential field method to achieve the trajectory control of the autonomous electric vehicle with in-wheel motors is proposed. Instead of strictly following a desired trajectory, this method can form a steering corridor with a desired tracking error tolerance, and the vehicle can be steered smoothly with less control effort. In the existing research, in order to realize the trajectory tracking of intelligent vehicles, many advanced control theories are applied to design trajectory tracking controllers, including model predictive control [7], robust control [8], and sliding mode control [9]. In addition, some researchers try to combine multiple control methods when designing vehicle trajectory following controllers, and integrate their advantages to improve the overall control performance. In order to ensure the stability of the autonomous vehicle during the trajectory-tracking process, [PERSON] et al. [10] designed an unscented Kalman filter to estimate the vehicle operating state, and applied the multi-constraint model predictive control method to the ideal trajectory tracking. In the existing research on the trajectory tracking control of intelligent vehicles, most of the research focused on the improvement of the trajectory tracking control performance, ignoring the problem of the vehicle's own stability during the trajectory tracking control process. In recent years, some researchers have also proposed some control methods that take into account the trajectory tracking effect and vehicle stability.