自适应双时域参数MPC的智能车辆路径规划与跟踪控制

Path planning and path tracking control for autonomous vehicle based on MPC with adaptive dual-horizon-parameters

为解决过度避障,基于车辆点质量模型,采用非线性模型预测控制(MPC)算法,设计了新型避障功能函数,建立自动驾驶车辆路径跟踪性能综合评价指标,得到最优预测时域和控制时域参数,设计自适应双时域MPC参数路径跟踪控制器。搭建规划层和控制层集成的联合仿真平台,进行仿真试验。结果表明:在180个障碍物点下,避障功能函数可避免过度避障,且计算时间仅增加0.294 ms。采用局部避障路径规划与路径跟踪控制集成结构时,在多静态障碍物场景与动态障碍物场景下,车速为65 km/h时,最大横向误差偏差减小0.169 m,最大横摆角速度减小3.196(°)/s。

This paper designed a novel obstacle avoidance function by using the nonlinear model predictive control(MPC)algorithm to solve the problem of excessive obstacle avoidance.Established a series of comprehensive evaluation index of vehicle path tracking performance,obtained the optimal prediction time domain and the control time domain parameters,and designed the adaptive dual time domain MPC parameters path tracking controller.A joint simulation platform integrating the planning layer and the control layer was built for simulation.The results show that under the condition of 180 obstacle points,the obstacle avoidance function can avoid excessive obstacle avoidance,and the calculation time only increases by 0.294 ms;The maximum lateral error deviation is reduced by 0.169 m with the maximum yaw rate being reduced by 3.196(°)/s when adopting the integrated structure of local obstacle avoidance path planning and path tracking control,in multiple static obstacle scenes and dynamic obstacle scenes,at the vehicle speed of 65 km/h.