To alleviate computational load on RSUs and cloud platforms, reduce communication bandwidth requirements, and provide a more stable vehicular network service, this paper proposes an optimized pinning control approach for heterogeneous multi-network vehicular ad-hoc networks (VANETs). In such networks, vehicles participate in multiple task-specific networks with asymmetric coupling and dynamic topologies. We first establish a rigorous theoretical foundation by proving the stability of pinning control strategies under both single and multi-network conditions, deriving sufficient stability conditions using Lyapunov theory and linear matrix inequalities (LMIs). Building on this theoretical groundwork, we propose an adaptive genetic algorithm tailored to select optimal pinning nodes, effectively balancing LMI constraints while prioritizing overlapping nodes to enhance control efficiency. Extensive simulations across various network scales demonstrate that our approach achieves rapid consensus with a reduced number of control nodes, particularly when leveraging network overlaps. This work provides a comprehensive solution for efficient control node selection in complex vehicular networks, offering practical implications for deploying large-scale intelligent transportation systems.

翻译：为减轻路侧单元（RSU）与云平台的计算负载、降低通信带宽需求并提供更稳定的车联网服务，本文针对异构多网络车载自组织网络（VANET）提出一种优化的牵制控制方法。在此类网络中，车辆参与多个任务专用网络，其耦合关系呈非对称性且拓扑结构动态变化。我们首先通过证明单网络与多网络条件下牵制控制策略的稳定性，建立了严格的理论基础，并利用李雅普诺夫理论和线性矩阵不等式（LMI）推导出充分的稳定性条件。基于此理论框架，我们提出一种自适应遗传算法，专门用于选择最优牵制节点，在有效平衡LMI约束的同时，优先考虑重叠节点以提升控制效率。在不同网络规模下的大量仿真实验表明，本方法能以更少的控制节点实现快速一致性，尤其在利用网络重叠结构时效果显著。本研究为复杂车辆网络中高效控制节点选择提供了完整解决方案，对大规模智能交通系统的部署具有实际指导意义。