Abstract: This paper focuses on the connected vehicle platoon control problem with structural uncertain disturbances and proposes a model predictive platoon control method based on robust equivalence transformation. First, the traditional vehicle platoon control model is described based on the vehicle kinematics state equation. Then, the affine structure uncertainty matrix is introduced into the state equation to develop the vehicle platoon control model under the minimum maximization paradigm. Based on the robust equivalence theory, the model is transformed into a computationally tractable epigraph optimization model under box uncertainty set. The sequential quadratic programming algorithm is then used to obtain the optimal platoon control scheme, and the control scheme is compared with the traditional vehicle platoon control scheme through simulation experiments to verify the effectiveness of the model. The results indicate that the proposed optimal platoon control method can resist the uncertain interference of system structureand ensure the vehiclesafety while achieving the goal of the stable platoon.

Key words: intelligent transportation, vehicle platooncontrol, model predictive control, connected vehicle, robust optimization, structure uncertainty

摘要： 为研究一类带有结构不确定干扰的网联车辆编队控制问题，考虑结构扰动服从盒式不确定集约束情况，提出一种基于鲁棒对等变换的模型预测编队控制方法。首先，基于车辆运动学状态方程，对传统车辆编队控制模型进行描述；其次，在状态方程中引入仿射结构不确定矩阵，构建最小最大化范式下的网联车辆编队控制模型；然后，在盒式不确定集下，基于鲁棒对等理论将该模型转化为计算上易于处理的上图优化模型；最后，采用序列二次规划算法获得最优编队控制方案，并通过仿真实验将该控制方案与传统车辆编队控制方案进行对比，以此验证模型的有效性。 结果表明：本文提出的最优编队控制方法能够抵御编队系统结构不确定干扰，在实现稳定编队目标的同时保障车辆的编队安全性。

关键词: 智能交通, 车辆编队控制, 模型预测控制, 网联车, 鲁棒优化, 结构不确定性