Optimized Design of DMPC for Trajectory Synchronization in Multi-Platoon Systems under Unidirectional Communication
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摘要: 针对多队列系统中的车辆编队协同控制问题, 研究了在单向通信下实现高效队列协同的方法. 首先, 建立了多队列系统的车辆动力学模型, 设计了以各队列领航车为根节点的全局通信拓扑, 以支持信息在队列内和队列间的传递. 基于此拓扑结构, 提出了一种包含队列内和队列间耦合约束的分布式模型预测控制方法, 分别针对队列内跟随车辆和队列间领航车辆制定了不同的局部优化问题, 以实现车辆和队列的并行优化. 其次, 通过Lyapunov稳定性分析, 证明了所提控制方法在单向通信拓扑下的渐近稳定性, 并推导了保证系统弦稳定性的参数设计条件. 数值仿真对比分析了三种队列间通信拓扑结构(领航车-尾车跟踪、领航车-领航车跟踪、领航车与领航车-尾车结合策略)的控制效果, 结果表明领航车与领航车-尾车结合策略在响应速度和系统稳定性之间取得了最佳平衡. 此外, 研究还验证了所提方法对不同惯性滞后参数和异质车辆特性的适应能力, 为多队列车辆系统的协同控制提供了理论基础和实用方法, 对智能交通系统的实际应用具有重要参考价值.Abstract: This paper addresses the coordinated formation control problem of vehicles in multi-platoon systems and investigates methods for achieving efficient platoon coordination under unidirectional communication structures. Firstly, the paper establishes vehicle dynamics models for multi-platoon systems and designs a global communication topology in which the lead vehicles of each platoon serve as root nodes to support information transmission within and between platoons. Based on this topology, a distributed model predictive control (DMPC) method incorporating coupling constraints for intra-platoon and inter-platoon interactions is proposed. Different local optimization problems are formulated for follower vehicles within platoons and lead vehicles between platoons, enabling parallel optimization of vehicles and platoons. Secondly, through Lyapunov stability analysis, the paper proves the asymptotic stability of the proposed control method under unidirectional communication topology and derives parameter design conditions that guarantee the string stability of the system. Numerical simulations compare the control performance of three inter-platoon communication topologies: lead-tail vehicle tracking, lead-lead vehicle tracking, and a combined lead-tail-lead vehicle strategy. The results show that the combined lead-tail-lead strategy achieves the best balance between response speed and system stability. Furthermore, the research verifies the adaptability of the proposed method to different inertial lag parameters and heterogeneous vehicle characteristics, providing theoretical foundations and practical approaches for the coordinated control of multi-platoon vehicle systems, with significant reference value for practical applications in intelligent transportation systems.
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图 3 领航车-尾车通信下队列状态变化
Fig. 3 Changes in the platoon status under the lead - tail vehicle communication
图 4 领航车-领航车通信下队列状态变化
Fig. 4 Changes in the platoon status under the lead - lead vehicle communication
图 5 领航车-尾车-领航车通信下队列状态变化
Fig. 5 Changes in the platoon status under the lead - tail - lead vehicle communication
图 6 不同控制策略在有初始状态误差条件下的动态响应
Fig. 6 Dynamic response of different control strategies with initial state error conditions
图 7 不同惯性滞后对系统动态响应特性的影响
Fig. 7 The impact of different inertia delays on the dynamic response characteristics of the system
图 8 车辆参数异质条件下控制算法的动态响应对比
Fig. 8 Comparative analysis of control algorithms＇ dynamic response under heterogeneous vehicle parameters
表 1 DMPC控制器权重矩阵值
Table 1 An example table in two column
$ \mathit{Q} $ $ \mathit{R} $ $ \mathit{F} $ $ \mathit{G} $ 跟随车$ i $ (0/5)$ {{I}_{2}} $ $ {{I}_{2}} $ $ 10{{I}_{2}} $ $ 8{{I}_{2}} $ 领航车$ k $ $ 10{{I}_{2}} $ $ {{I}_{2}} $ $ 10{{I}_{2}} $ (0/1)$ 10{{I}_{2}} $ 
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