5. Summary and outlook
(1) Path tracking control is one of the research hotspots in the field of unmanned vehicles, and the preliminary research is usually based on the methods of vehicle kinematics and dynamics, using information such as front wheel turning angle and vehicle position and heading angle error and road curvature for path tracking control.
(2) In the research of path tracking control for low-speed conditions, the more applied are PID, PP, Stanley and MPC algorithms. By improving and using the related algorithms in combination, the influence of the front wheel rotational speed constraint on the path tracking accuracy is reduced. As the path tracking control in low-speed conditions requires low real-time, the current research basically meets the needs of low-speed path tracking control, and has more mature applications in automatic parking and special vehicles.
(3) In the research of path tracking control in high-speed conditions, the more applied are LQR, MPC, ADRC, H∞, SMC and RL algorithms. In order to achieve the purpose of controlling the path tracking accuracy and vehicle driving stability, and to solve the coupling relationship of optimization objectives, the current research usually combines two or more algorithms according to the actual needs to achieve complementary advantages and disadvantages.
(4) MPC algorithm has become a mainstream method for path tracking control of driverless vehicles at present, but its computational real-time and ability to handle system uncertainty are still the future development direction.
(5) Since the decision indexes of speed regulation and weight distribution are usually related to environmental factors such as ground adhesion coefficient and lateral wind speed, and there is less research in this direction by scholars concerned, with the parallel development of machine learning theory and unmanned vehicle computing power, its application under extreme working conditions is also an important research direction.
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