文章摘要
基于模型预测控制算法的车辆编队研究
Research on Vehicle Platoon Based on Model Predictive Control Algorithm
投稿时间:2024-01-16  修订日期:2024-05-13
DOI:
中文关键词: 智能交通  协同自适应巡航  车辆编队控制  模型预测控制
英文关键词: intelligent transportation  cooperative adaptive cruise  vehicle platoon control  model predictive control
基金项目:安徽省高校自然科学基金重点项目(2022AH050252)
作者单位邮编
李启朗 安徽建筑大学 230000
郭文静* 安徽建筑大学 230000
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中文摘要:
      由于受到环境等多重约束条件影响等问题,刻画自动驾驶车辆运动的模型不确定性较大。根据协同自适应巡航控制(Cooperative Adaptive Cruise Control,CACC)框架,搭建车辆间纵向运动学模型,并建立相应的离散状态空间方程,利用模型预测控制(Model Predictive Control,MPC)算法设计了编队控制器。采用模型预测控制方法,对前车或车队未来状态进行预测,以优化跟随车辆的运动控制。通过车间通信获取跟随车辆与前车的信息,从而决策出跟随车辆的期望加速度。在选择适当的控制器参数的基础上,使得跟随误差得以最小化。通过对三辆车的编队仿真实验,验证了所提出编队控制器的有效性。该方法以加速度作为控制量,更符合实际应用场景。针对不同车头时距的情况进行了仿真研究,结果表明车头时距越小,车辆跟踪效果越佳。
英文摘要:
      Due to various constraints, including environmental factors, there is significant uncertainty in modeling the motion of autonomous vehicles. In accordance with the Cooperative Adaptive Cruise Control (CACC) framework, this study constructs a longitudinal kinematic model for inter-vehicle motion and establishes corresponding discrete state-space equations. A formation controller is designed using the Model Predictive Control (MPC) approach to optimize the motion control of the following vehicle. The MPC method predicts the future states of the lead vehicle or the entire fleet, utilizing model predictive algorithms. Inter-vehicle communication is employed to obtain information about the following vehicle and the lead vehicle, aiding in determining the desired acceleration for the following vehicle. By selecting appropriate controller parameters, the following error is minimized. Simulation experiments involving three vehicles validate the effectiveness of the proposed formation controller. The method employs acceleration as the control variable, making it more suitable for real-world applications. Simulation studies for different headways indicate that smaller headways result in better vehicle tracking performance.
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