Improved Linear Quadratic Regulator Lateral Path Tracking Approach Based on a Real-Time Updated Algorithm with Fuzzy Control and Cosine Similarity for Autonomous Vehicles

• Published in: Electronics (Basel) Vol. 11; no. 22; p. 3703
• Main Authors: Wang, Zhaoqiang, Sun, Keyang, Ma, Siqun, Sun, Lingtao, Gao, Wei, Dong, Zhuangzhuang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Algorithms; Autonomous vehicles; Computational efficiency; Control algorithms; Control methods; Control theory; Controllers; Coordinates; Driverless cars; Feedforward control; Fuzzy control; Fuzzy logic; Genetic algorithms; Kinematics; Lagrange multiplier; Lateral stability; Linear quadratic regulator; Mathematical models; Neural networks; Path tracking; Real time; Similarity; Steering; Tracking control; Velocity; Wheels; China
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11223703

Path tracking plays a crucial role in autonomous driving. In order to ensure the real-time performance of the controller and at the same time improve the stability and adaptability of the path tracking controller, a lateral path control strategy based on the improved LQR algorithm is proposed in this paper. To begin with, a discrete LQR controller with feedforward and feedback components is designed based on the error model of vehicle lateral dynamics constructed by the natural coordinate system. Then, a fuzzy control method is applied to adjust the weight coefficients of the LQR in real time according to the state of the vehicle. Furthermore, an update mechanism based on cosine similarity is designed to reduce the computational effort of the controller. The improved LQR controller is tested on a joint Simulink–Carsim simulation platform for a two-lane shift maneuver. The results show that the control algorithm improves tracking accuracy, steering stability and computational efficiency.