To address the challenge of determining the weight matrix in the Linear Quadratic Regulator (LQR) algorithm for intelligent vehicle path tracking control, a LQR path-following controller based on genetic algorithm (GA) optimization is proposed. Firstly, a 2-DOF vehicle path tracking error dynamics model is established, and a LQR controller with preview is designed to calculate the optimal front wheel angle output of the vehicle; Then, for the determination of LQR controller parameters, a GA optimization strategy with vehicle lateral error, yaw error, and output front wheel angle as the objective function is designed. The optimization solution yields the optimal weight matrices Q and R; Finally, in the environment of MATLAB/Simulink and CarSim joint simulation, the tracking performance and robustness of LQR controller optimized by the genetic algorithm is verified. The results show that: under the double-lane-changing road and continuous lane-changing conditions, the optimized GA_LQR control vehicle’s lateral error peak value is reduced by 86.6% and 84.2%; the heading error peak value is reduced by 17.7% and 14.6%, respectively; the front wheel angle is smaller. The vehicle’s lateral and yaw tracking capabilities are enhanced as well. Moreover, the vehicle still exhibits good path tracking, speed tracking, and driving stability under different speeds on double-sine roads.

Intelligent vehicle  /  Path tracking  /  Genetic Algorithm(GA)  /  Linear Quadratic Regulator(LQR) Control