车联网环境下车辆最优车速闭环快速模型预测控制

发布时间：2018-08-05 11:03

【摘要】：为解决智能交通系统中多辆车的最优车速预测问题,提高车辆燃油经济性及控制算法的执行效率,提出一种基于交通信号灯正时(SPAT)的方法来计算目标车速范围,避免车辆红灯停车;提出基于最优巡航车速和最优加速度跟随的模型预测控制建模方法;提出考虑效率反馈的闭环车速预测控制方法并采用快速模型预测求解最优目标车速。基于云计算的软件在环仿真结果表明,提出的控制方法可以避免车辆红灯怠速以及发生碰撞;相比于基准方法 1和基准方法 2,平均燃油经济性分别提高6.44%和37.1%;相比于基准方法1,系统平均驱动效率提高7.13%;快速模型预测程序执行效率为模型预测的13倍,且每一步长的实际计算时间小于计算步长,可以实现实时控制。
[Abstract]:In order to solve the problem of optimal speed prediction of multiple vehicles in intelligent transportation system and improve vehicle fuel economy and the efficiency of control algorithm, a method based on traffic signal timing (SPAT) is proposed to calculate the target speed range. A model predictive control method based on optimal cruising speed and optimal acceleration tracking is proposed. A closed-loop speed predictive control method considering efficiency feedback is proposed and the fast model is used to predict and solve the optimal target speed. The simulation results of cloud computing software in the loop show that the proposed control method can avoid the idling speed of vehicle red light and collision. Compared with baseline method 1 and benchmark method 2, the average fuel economy is increased by 6.44% and 37.1%, respectively; compared with benchmark method 1, the average driving efficiency of the system is increased by 7.13%; and the efficiency of the fast model prediction program is 13 times higher than that of the model prediction. And the actual calculation time of each step is less than the calculation step, which can realize real-time control.
【作者单位】：合肥工业大学汽车与交通工程学院;克莱姆森大学国际汽车研究中心;
【基金】：国家科技支撑计划资助项目(2013BAG08B01) 美国国家自然科学基金资助项目(1544910)
【分类号】：U469.7

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