Intelligent Vehicle Trajectory Tracking Control Based on DBO-LQR and MPC

To improve the accuracy and stability of intelligent vehicle trajectory tracking, a transverse and longitudinal control strategy based on dung beetle optimization algorithm (DBO) optimized linear quadratic regulator (LQR) with model predictive control (MPC) was proposed. A vehicle dynamics model and a transverse error model based on Frenet coordinate system were constructed, the transverse LQR controller with feedforward was designed, and the dung beetle optimization algorithm was used to determine the weight coefficient of the LQR controller. The longitudinal speed and position tracking were realized based on MPC, and the longitudinal speed was used to connect lateral and longitudinal controllers, while controlling the speed and steering of the vehicle at the same time. Finally, simulation experiments were conducted on different working conditions using the CarSim and Matlab/Simulink joint simulation platform to verify the effectiveness of the proposed strategy. The results show that the maximum lateral tracking error of the vehicle is less than 0.010 m and the heading deviation is within 0.025 0 rad under three working conditions of parking on urban road, lane changing on urban roads, and lane changing on highways. The changes of transverse angular velocity and front wheel angle are relatively smooth without obvious jitter, and the proposed strategy can effectively improve the accuracy and stability of the tracking trajectory of the vehicle.