针对复杂车载网络，提出自适应遗传算法选控节点，提升稳定性和效率。
Adaptive Genetic Selection based Pinning Control with Asymmetric Coupling for Multi-Network Heterogeneous Vehicular Systems
- 用自适应遗传算法动态选择关键控制节点，兼顾网络重叠特征。
- 在多网络场景下，减少控制节点数30%以上，实现快速共识。
- 适合大规模智能交通系统部署，尤其适用于异构动态网络。
为减轻路侧单元（RSUs）和云平台的计算负载、降低通信带宽需求，并提供更稳定的车载网络服务，本文针对具有非对称耦合和动态拓扑的异构多网络车载自组织网络（VANETs），提出一种优化的钉控策略。首先基于李雅普诺夫理论和线性矩阵不等式（LMIs）严格证明了单网与多网条件下钉控策略的稳定性，推导出充分稳定性条件。在此理论基础上，设计了一种自适应遗传算法，用于选择最优钉控节点，在满足LMIs约束的同时优先考虑网络重叠节点，显著提升控制效率。大量仿真结果表明，该方法在不同规模网络中均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时效果更优。本工作为复杂车载网络中的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意义。
原文摘要 · Abstract (English)
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.
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