为了增强智能车的轨迹跟踪能力和稳定性，提出了基于预瞄理论和线性二次调节器（LQR）的横纵向控制策略。首先建立了车辆二自由度动力学模型和车-路误差动力学模型，其次基于预瞄控制理论，在LQR的状态向量增加未来的道路曲率这一扰动，并依据最优理论解析增广LQR问题，得到控制量的解析解，考虑智能车的动力学对最优控制进行约束，提高智能车对极限车况的适应性，最后采用模拟退火算法（simulated annealing algorithm，SAA）对预瞄时间进行优化求解，获取不同车速和道路摩擦系数下的最优预瞄时间。分析基于预瞄理论的带约束增广LQR算法构成的闭环控制系统的稳定性，验证算法的可行性。通过Carsim/Simulink联合平台对前述轨迹跟踪控制算法进行仿真验证，仿真结果表明：提出的基于预瞄理论的带约束增广LQR算法具有优秀的轨迹跟踪能力、稳定性和对车速的鲁棒性。

This study proposes a lateral and longitudinal control strategy for intelligent vehicles based on the preview control theory and linear quadratic regulator (LQR) to enhance the trajectory tracking ability and stability. The two-degree-of-freedom dynamic model of the vehicle and the road-vehicle error dynamic model is established, and the future road curvature is incorporated as a disturbance into the LQR state vector using the preview control theory. An augmented LQR problem is solved according to the optimal theory to obtain the analytical solution of the control quantity. This strategy also enhances the adaptive ability of the intelligent vehicle to extreme conditions by taking into account the dynamic constraints. The preview time is optimized using the simulated annealing algorithm to obtain the optimal preview time under different vehicle speeds and road friction coefficients. The stability of the closed-loop control system composed of the new algorithm is analyzed to verify its feasibility. Simulation results on the Carsim/Simulink joint platform demonstrate that it has excellent trajectory tracking ability, stability, and robustness to the vehicle speed. The proposed strategy has the potential to significantly advance the field of intelligent vehicle control and improve the safety and efficiency of transportation systems.

四川省科技计划资助（基金号：2023YFQ0026）