时延MPC自主车辆协同控制算法与仿真

协同自适应巡航控制（CACC）系统中车辆纵向运动的上下位分层控制器结构，上位控制器采用状态空间模型预测控制算法，利用期望距离以及车辆与环境的实时信息决策出被控车辆运动的期望加速度。下位控制器根据期望加速度，求解发动机节气门开度或制动压力。车辆的执行器时延会对系统的稳定性产生很大的影响。根据动态矩阵控制算法对纯滞后对象的补偿作用，提出一种改进的模型预测控制算法，并与PID控制算法（下位控制器）相结合形成自主车辆纵向运动的上下位分层控制器，以补偿车辆的执行器时延带来的影响。通过SIMULINK/CARSIM联合仿真平台对所设计的算法进行了仿真研究，仿真结果表明所设计算法减小了CACC系统车辆在跟随过程中的速度跟踪误差以及间距误差，提高了系统的稳定性法。

Autonomous Vehicle Cooperative Control Algorithm and Simulation Research Based on MPC with Time-Delay

In the hierarchical longitudinal control structure of the Cooperative Adaptive Cruise Control（CACC） system, the upper level controller uses the state space model predictive control algorithm to determine the desired longitudinal acceleration of the target vehicle by the expected distance and the real-time information of the vehicles and the environment. The lower level controller determines the throttle or brake commands required to track the desired acceleration. The actuator delay of a vehicle has a significant impact on the stability of the system. In view of the compensation characteristic of the dynamic matrix control algorithm on the pure lag object, an improved model predictive control algorithm is proposed by this paper and combined with the PID control algorithm（the lower level controller） to form the hierarchical longitudinal control structure, which is used to compensate the effect of vehicle actuator delay. The designed algorithm is simulated by SIMULINK/CARSIM co-simulation platform. The simulation results show that the designed algorithm can reduce the speed tracking error and the spacing error of the CACC system in following process and improve the stability of the system.