复杂路况下无人驾驶路径跟踪模型预测控制研究

Research on Model Predictive Control of Autonomous Vehicle Path Tracking Under Complex Road Condition

为了提高无人驾驶车辆在直角转弯、连续弯道和弧形弯的复杂路况下路径跟踪精度、行驶稳定性与安全性,提出了一种改进的模型预测控制算法。该改进算法是根据行驶路径弯曲度确定车辆在平坦路面上不发生滑移的最大纵向速度,即车辆纵向速度不是假定恒定值。基于模型预测控制,建立车辆运动学模型,设置以速度和前轮转角为约束条件,设计以位置偏差和控制增量为目标函数,获得最优前轮转角和行驶速度。最后,借助某新能源汽车有限公司提供的无人驾驶车辆平台与测试场地,试验对比分析了在复杂路况下改进的模型预测控制算法与纵向速度恒定的模型预测控制算法时车辆路径跟踪效果,试验验证了改进模型预测控制算法的有效性与优越性,保证了车辆的路径跟踪精度、行驶平稳性与安全性。

To improve the path tracking accuracy,driving stability and safety of autonomous vehicle under complex road conditions of right angle turn,continuous curve and arc curve,an improved model predictive control(MPC)algorithm is proposed. The salient feature of the improved MPC is that the maximum longitudinal speed of vehicle without sliding on the flat road is determined by the curvature of the travel path,that is,the longitudinal speed of the vehicle is not assumed to be constant. Based on model predictive control,the vehicle kinematics model is established. Speed and front wheel angle are set as constraints. And position deviation and control increment are designed as the objective function to obtain the optimal front wheel angle and driving speed. Finally,with the experimental platform and test site provided by a new energy vehicle company,the path tracking effect of the improved MPC under complex road conditions and the model predictive control algorithm with constant longitudinal speed is compared and analyzed. The experimental results verify the effectiveness and superiority of the improved MPC,and indicate that the accuracy of the path tracking,the driving stability and the safety of vehicle are guaranteed.