基于混合控制策略的智能汽车路径跟踪控制研究

Research on Path Tracking Control of Intelligent Vehicles Based on Hybrid Control Strategy

针对线性二次调节器(LQR)算法的智能汽车路径跟踪控制器权重矩阵确定困难的问题,提出一种基于遗传算法(GA)优化的LQR路径跟踪控制器。首先,建立2自由度车辆路径跟踪误差动力学模型,设计有预瞄功能的LQR控制器,计算车辆最优的前轮转角输出;然后针对LQR控制器参数的确定,设计以车辆的横向误差、航向误差和输出前轮转角为目标函数的遗传算法优化策略,优化求解得到最优权重矩阵Q和R;最后,在MATLAB/Simulink与CarSim联合仿真的环境下,验证遗传算法优化后的LQR控制器的跟踪效果和鲁棒性。结果表明:在双移线道路和连续换道工况下,优化后的GA_LQR控制车辆的横向误差峰值分别降低了86.6%和84.2%,航向误差峰值分别降低了17.7%和14.6%,输出较小的前轮转角,提高了车辆横向和航向的跟踪能力,且车辆在不同速度的双移线道路工况下,仍有较好的路径跟踪、速度跟踪和行驶稳定性。

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.