融合时延和参数不确定性的智能汽车轨迹跟踪鲁棒协同控制

Robust Trajectory Tracking and Yaw Stability Control Strategy on Communication Time-Delay and Parameter Uncertainty for Intelligent Vehicles

针对参数不确定性和网络时滞效应引起的轨迹跟踪控制性能衰退问题,提出了基于状态误差快速收敛的输出反馈鲁棒模型预测控制策略.根据汽车参数的不确定性来源和网络信号的随机时延特性,构建了包含两类不确定项的多胞体控制模型,并通过鲁棒状态观测器实现对状态变量的精确观测,结合采用快速收敛约束设计的鲁棒模型预测控制器,有效提高了车辆的轨迹跟踪精度和横摆稳定性.测试结果表明,提出的策略可以有效消除两类不确定性对跟踪控制的影响;相对于传统的线性模型预测控制和鲁棒模型预测控制方法,其轨迹跟踪精度分别提高了83.70%和19.41%,横摆稳定性分别提高了72.88%和40.74%,实车试验表明其具备良好的实时性和可行性.

To solve the degradation problems existing in vehicle trajectory tracking control due to the parameter uncertainty and network delay,a predictive control strategy was proposed for output feedback robust model based on fast convergence of state error.Firstly,considering the uncertainty sources of vehicle parameters and the random delay characteristics of network signals,a multi-center control model was constructed with the incorporation of two type uncertain terms.Then,a robust observer was taken to observe the state variables accurately,and a predictive controller with fast constraints was designed effectively for the robust model to improve trajectory tracking accuracy and yaw stability of the vehicles.The test results show that the proposed strategy can effectively eliminate the impact of two type uncertainties on tracking control.Compared with the traditional linear model predictive control and robust model predictive control methods,the trajectory tracking accuracy of the new proposed robust model can be improved by 83.70%and 19.41%respectively,and Yaw stability can be improved by 72.88%and 40.74%respectively.The actual vehicle tests show that the proposed strategy can provide a better real-time performance and feasibility.