基于DBO-LQR和MPC的智能车轨迹跟踪控制

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

为提高智能车辆轨迹跟踪的精度和稳定性,提出1种基于蜣螂优化(DBO)算法优化的线性二次型调节器(LQR)与模型预测控制(MPC)的横向、纵向控制策略。构建车辆动力学模型和基于Frenet坐标系下的横向误差模型,设计带有前馈的横向LQR控制器,利用蜣螂优化算法确定LQR控制器权重系数;基于MPC实现纵向速度和位置的跟踪,利用纵向速度联结横向控制器与纵向控制器,同时对车辆的速度和转向进行控制;最后基于CarSim和Matlab/Simulink联合仿真平台在不同道路工况下进行仿真实验,验证所提策略的有效性。结果表明:在城市道路泊车、城市道路换道、高速公路换道3种工况下,车辆的最大横向跟踪误差均小于0.010 m、航向偏差在0.0250 rad内;横摆角速度及前轮转角变化比较平稳、无明显抖动,所提策略可有效提高车辆跟踪轨迹的精度和稳定性。

To improve the accuracy and stability of intelligent vehicle trajectory tracking,a transverse and longitudinal control strategy based on dung beetle optimization(DBO)algorithm 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 DBO 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 0.0250 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.