Design, Validation and Comparison of Path Following Controllers for Autonomous Vehicles

Abstract

As one of the core issues of autonomous vehicles, vehicle motion control directly affects vehicle safety and user experience. Therefore, it is expected to design a simple, reliable, and robust path following the controller that can handle complex situations. To deal with the longitudinal motion control problem, a speed tracking controller based on sliding mode control with nonlinear conditional integrator is proposed, and its stability is proved by the Lyapunov theory. Then, a linear parameter varying model predictive control (LPV-MPC) based lateral controller is formulated that the optimization problem is solved by CVXGEN. The nonlinear active disturbance rejection control (ADRC) method is applied to the second lateral controller that is easy to be implemented and robust to parametric uncertainties and disturbances, and the pure pursuit algorithm serves as a benchmark. Simulation results in different scenarios demonstrate the effectiveness of the proposed control schemes, and a comparison is made to highlight the advantages and drawbacks. It can be concluded that the LPV-MPC has some trouble to handle uncertainties while the nonlinear ADRC performs slight worse tracking but has strong robustness. With the parallel development of the control theory and computing power, robust MPC may be the future direction.

Research on path following algorithm of intelligent vehicle based on Stanley compensatory pure pursuit

“…It can be seen that it is necessary to correct the front wheel angle value of the intelligent vehicle, so as to ensure that the intelligent vehicle runs on the predetermined path [10]. The above Figure 6 shows the model of Stanley compensatory pure pursuit and following control algorithm, establishing the coordinate system, and (𝑥 ，𝑦 )为 is the center of the rear axle of the smart car; (𝑥 ，𝑦 )为 is the center of the front axle of the intelligent vehicle; 𝑝 ，𝑝 is the preview point on the desired path; 𝑉 is the longitudinal speed of the intelligent vehicle; 𝑒 is the lateral deviation between the center of the front axle and the desired path; 𝜃 is the desired heading angle; 𝛿 is the front wheel angle value; 𝑅 is the distance between the rear axle center and the circular motion center; 𝑑 is the lateral distance between the preview point and the circular motion center, which is obtained from the mathematical theorem:…”

A Review of Autonomous Vehicle Path Tracking Algorithm Research

“…For example, Kang et al proposed an improved selfadverse control (IADRC) method, including an improved extended state observer and an LQR-based error compensator, and designed a vehicle path tracking controller based on IADRC considering lateral stability, and the simulation results showed that the controller has better path tracking effect and robustness against disturbances [87].Yun et al proposed a new method for high-speed Wang et al proposed an ANN-based ADRC method for high-speed automatic emergency vehicle avoidance technology, and the simulation results proved that the method has better path tracking accuracy and robustness at different vehicle speeds, and the tracking error is smaller at 60 km/h lateral wind interference at 100 km/h vehicle speed [88].Wang et al proposed an integrated feedforward-feedback and ADRC compensation lateral control algorithm and achieved better tracking effect and stability [89]. And Yang et al verified that the tracking performance of nonlinear ADRC is slightly worse, but it has strong robustness [90].…”

Autonomous vehicle tracking control for a curved trajectory

“…The control of a vehicle's motion is a critical part of autonomous vehicles, as it directly impacts the vehicle's safety and the satisfaction of its passengers. So, it is crucial to develop a path-following controller that overcomes problems in challenging environments [1].…”