| 李启朗,郭文静.基于模型预测控制算法的车辆编队研究[J].安徽建筑大学学报,2024,32(5):40-45 |
| 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 |
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| 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:安徽省高校省级自然科学研究项目（2022AH050252） |
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| 摘要点击次数: 2293 |
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| 中文摘要: |
| 为应对自动驾驶车辆运动的模型不确定性问题，根据协同自适应巡航控制（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。采用模型预测控制（MPC）方法，预测前车或车队未来状态，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，从而得出跟随车辆的期望加速度。通过对五辆车的编队仿真实验，验证了所提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。与PID算法对比，该方法的车辆最大加速度降低了19.23%，最大位置跟随误差降低了63.51%。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: |
| To address the uncertainty in the dynamics of autonomous vehicle model, a longitudinal kinematic model for vehicle-tovehicle interactions is developed within the framework of cooperative adaptive cruise control (CACC), along with the correspondingdiscrete state-space equations. A model predictive control (MPC) approach is utilized to forecast the leading vehicle and the vehicleplatoon, in order to optimize the motion control of the following vehicle. The vehicle-to-vehicle communication information is obtainedto determine the desired acceleration for the following vehicle. The effectiveness of the proposed platoon controller is validated throughsimulation experiments involving a platoon of five vehicles. This method, which takes acceleration as the control variable, is better suitedto practical applications. Compared to the PID algorithm, this method achieves a 19.23% reduction in maximum vehicle accelerationand a 63.51% reduction in maximum position tracking error. Simulation studies under different headway times indicate that shorterheadway times result in improved vehicle tracking performance. |
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