基于门控循环单元优化的轨迹跟踪控制方法

Trajectory Tracking Control Method Based on GRU Optimization

为提高智能汽车轨迹跟踪精度,以多点预瞄模型为基础,设计了一种基于门控循环单元(GRU)神经网络优化的轨迹跟踪模型。首先,在车辆二自由度模型上,基于预瞄理论建立了3种预瞄轨迹跟踪模型,仿真验证结果表明,多点预瞄模型跟随效果最佳。然后,将多点预瞄横向位移偏差和转向盘转角等参数作为GRU神经网络的输入,经过训练后,输出优化后的转向盘转角控制车辆的行驶方向。验证结果表明,与多点预瞄模型相比,经GRU优化的轨迹跟踪模型在双移线路径和S型曲线路径下均有更好的跟踪效果。

Based on the multi-point preview model,a trajectory tracking model optimized by Grated Re-circulated Unit(GRU)neural network was designed to improve trajectory tracking accuracy of intelligent vehicle.Firstly,on the vehicle’s 2 degree of freedom model,3 preview trajectory tracking models were established based on the preview theory.The simulation results show that the multi-point preview model has the best tracking effect.Then,the parameters such as multipoint preview lateral displacement deviation and steering wheel angle were used as the inputs of GRU neural network.After training,the optimized steering wheel angle was as output to control the driving direction of the vehicle.The verification results show that the trajectory tracking model optimized by GRU has better tracking effect under double shift path and S-curve path.