基于MFAPC的无人驾驶汽车路径跟踪方法

Path Tracking Method of Unmanned Vehicle Based on MFAPC

为提高无人驾驶汽车转向系统对目标路径的跟踪精度,提出一种新的无人驾驶汽车路径跟踪方法。介绍基于跟踪预瞄点的无人驾驶汽车横向控制方案,给出系统的动态线性化数据模型及其多步预测方程,并采用最小二乘法推导出伪梯度向量的估计和预测方程。结合无模型自适应控制与预测控制的优点,能够通过滚动优化策略进行反复的在线计算,从而得到较好的动态性能。基于CarSim/Simulink联合仿真平台,在车速分别为5m/s、20m/s时进行验证,结果表明,与基于车辆动力学的MPC方案相比,该方法具有更好的跟踪效果。

In order to improve the tracking precision of the unmanned vehicle steering system to the target path,this paper proposes a new unmanned vehicle path tracking method.It introduces the lateral control scheme of unmanned vehicle based on tracking preview point,and gives the dynamic linearized data model of the system and its multi-step prediction equation.The estimation and prediction model of pseudo-gradient vectors is derived by using the method of least squares.Adopting the advantages of model-free adaptive predictive control,it can perform repeated online calculations through rolling optimization strategies,so as to obtain better dynamic performance.Based on the CarSim/Simulink co-simulation platform,the method is verified when the vehicle speed is 5 m/s and 20 m/s.Results show that compared with the MPC scheme based on vehicle dynamics,this proposed method has better tracking performance.