# Improved ADRC-Based Autonomous Vehicle Path-Tracking Control Study Considering Lateral Stability
## 1 Introduction
By summary and analysis, autonomous vehicle path-tracking control schemes designed in the literature reported above need to be based on accurate models of vehicle dynamics or accurate related parameters; however, there are always simplified parts in models or parameter uncertainties, and external disturbances in autonomous vehicle systems, etc. ; these factors may lead to the robustness of the control system that is affected. Therefore, [PERSON] [18] focused on the uncertainty of the system and designed a path-tracking controller based on the sliding-mode control method; the results showed that the controller has robust capability against system uncertainties. As is well known in the antidisturbance problem, active disturbance rejection control (ADRC) has received much attention and application because of its advantage of being able to estimate the disturbance value and compensate for the disturbance in the feedback. [PERSON] [19], [PERSON] [20], [PERSON] [21], and other scholars [22] designed autonomous vehicle path-tracking control systems based on ADRC; the results show that the controllers have strong robustness against parameter uncertainty and external disturbance. Among them, [19] adopts the ADRC control method; [20] adopts the control method of combined MPC and ADRC; [21] adopts the method of combined differential flatness and ADRC; the method of combined linear active disturbance rejection control (LADRC) and quantitative feedback theory is adopted in [22]. However, it can be seen that the nonlinear functions used in ADRC are piecewise functions and do not have continuity, which may lead to jittering of the control input, which is the focus of this paper. In summary, this paper focuses on the autonomous vehicle path-tracking control problem considering lateral stability based on the IADRC method, proposes an IADRC control scheme for the problems such as complexity of ADRC parameter tuning and control input jittering phenomenon. The control scheme uses a novel nonlinear function with continuity to solve the problem of control input jittering; the linear quadratic regulator (LQR) control method is used as the error compensator in the classic ADRC, so that the parameters to be tuned in the error compensator have a clear physical meaning and the parameter tuning process is optimized. To improve the lateral stability of the vehicle, a DYC controller is designed to output an external yaw moment. A control distributor is designed based on the concept of control allocation (CA) by means of differential braking to distribute the external yaw moment to the four wheels in a reasonable and optimal way. Finally, the control scheme is evaluated in the form of a CarSim/Simulink cosimulation. The main contributions of this paper include the following:1. An IADRC method is proposed in which a new continuous nonlinear function is proposed to replace the classic piecewise function. 2. An autonomous vehicle path-tracking control scheme is proposed based on the IADRC; the control system can estimate the real-time action value of the parameter uncertainty and disturbance, and then compensation in the feedback realizes the antidisturbance capability of the controller and improves the robustness of the control system against parameter uncertainty and external disturbance. 3. Using the CA concept, a control distributor is designed to optimize and coordinate of each wheel in real-time; the external yaw moment used to improve the lateral stability of the vehicle is realized in the form of differential braking. The structure of the rest of this paper is shown as follows. In Section 2, the vehicle model including dynamics and path-tracking components are built for the purpose of control system design, followed by an improved ADRC method, which is introduced in Section 3. In Section 4, an autonomous vehicle path-tracking controller is designed, and the effectiveness of the control scheme is verified in Section 5, followed by a comprehensive conclusion in Section 6.