基于模型预测控制的协同式自适应巡航控制系统

摘要/Abstract

摘要： 在城市交通工况中，车辆的驾驶行为对其乘坐舒适性及燃油消耗有着很大的影响。因此提出一种在包含交通灯等信息的交通工况下的协同式自适应巡航控制系统，通过减少不必要的速度保持或加速来提升性能。系统通过处理当前交通信息的数据判断跟踪目标类别，运用模型预测控制来预测前车或车队未来状态，对不同的前方目标采用不同的权值来计算最优控制输入。通过控制车辆保持安全距离并在优化速度下行驶以实现多目标的优化。利用CarSim和Simulink联合仿真，仿真结果显示该控制系统在保证安全的前提下实现了主动的速度调节及目标的切换,在指定仿真工况中对比线性二次调节算法，加速度峰值、加速度变化率峰值及燃油消耗均有所降低，乘坐舒适性和燃油经济性得到较大提升。

关键词: 协同式自适应巡航系统, 多目标优化, 模型预测控制, 交通信息

Abstract: Driving behaviors of the vehicle had a great influence on the riding comfort and fuel consumption in urban traffic conditions. Therefore, CACC system was designed based on the traffic conditions containing the informations such as traffic lights to improve the performance by reducing unnecessary speed keeping or acceleration. The type of tracking target was judged by processing current traffic information data and preceding vehicle or string of preceding vehicles' future states were predicted, relevant weights to different targets were used to calculate the optimal control inputs by means of MPC. Multi-objective optimization was realized by controlling the vehicles to keep a safe distance and cruising under the optimized speeds. The simulation results of CarSim and Simulink show that the control system takes the initiative to adjust the speed and to switch the target guarantee driving safety, and reduces fuel consumption, the peak value of acceleration and jerks which improves the ride comfort and fuel economy.

Key words: cooperative adaptive cruise control(CACC) system, multi-objective optimization, model predictive control(MPC), traffic information

中图分类号:

U463.6

孙涛, 夏维, 李道飞. 基于模型预测控制的协同式自适应巡航控制系统[J]. 中国机械工程.

SUN Tao, XIA Wei, LI Daofei. CACC System Based on MPC[J]. China Mechanical Engineering.

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