车辆轨迹模型预测控制器的算法研究

Research on Algorithm of Vehicle Trajectory Model Predictive Controller

为提高无人驾驶汽车行驶控制中的跟踪精度和车辆稳定性,提出了一个改进的模型预测控制器(MPC)。首先,为解决高速转弯工况下轮胎侧偏刚度的强非线性特性导致的车辆失稳问题,建立了轮胎动态侧偏刚度估计器进行实时估计;在此基础上,利用改进粒子群算法对现有模型预测控制器的时域参数进行优化,以提高算法跟踪精度和稳定性;最后,为验证控制器的效果,选取合适的工况进行了联合仿真测试。结果表明,改进控制器在高速转弯的工况下,跟踪精度误差降为4.9%,横摆角误差降为2.6%,比传统模型预测控制器分别提高了67.8%和62.3%,比模糊控制算法优化的控制器分别提高了55.8%和58%。

An improved model predictive controller(MPC)was proposed to improve the tracking accu-racy and vehicle stability of driverless vehicles.Firstly,in order to solve the vehicle instability problem caused by the strong nonlinear characteristics of tire side stiffness under high-speed turning conditions,a tire dynamic side stiffness estimator was established for real-time estimation.On this basis,the improved particle swarm optimization algorithm was used to optimize the time domain parameters of the existing model prediction controller to improve the tracking accuracy and stability of the algorithm.Finally,in order to verify the effect of the controller,the appropriate working conditions were selected for co-simula-tion test.The results show that the tracking accuracy error and yaw Angle error of the improved control-ler are reduced to 4.9% and 2.6% respectively under the condition of high-speed turning,which are 67.8% and 62.3% higher than those of the traditional model forecast controller,and 55.8%and 58%higher than those of the controller optimized by fuzzy control algorithm.