针对小型无人机集群在近地环境中受风速扰动影响导致跟随性能下降的问题，本文提出一种基于风速分级的混合控制器调度策略，实现了编队系统全工况下的鲁棒性与控制效率。首先，建立风速扰动模型，将合成扰动分解为慢变均值风、湍流分量及未知有界扰动，并基于蒲福风级（Beaufort scale）对风力进行分级。在此基础上，设计三级递进式控制架构：在低风速（1-2级）下，基于强化学习框架设计最优控制律，兼顾能耗与跟踪精度；在中风速（3级）下，引入分布式协同扩展状态观测器，通过网络协同提升扰动估计精度；在高风速（4-5级）下，集成自适应积分滑模补偿技术，有效抑制剧烈扰动。基于领导-跟随一致性原则，结合李雅普诺夫方法与输入-状态稳定性理论构建统一分析框架，设计了带迟滞效应的切换策略，从而避免风级边界处的控制器抖振。理论分析证明了编队状态具有全局渐近稳定与有界收敛。仿真实验表明，所提方法在不同风速和通信拓扑下均能有效抑制扰动，并在能耗效率、扰动抑制能力和动态响应速度方面具有优越性，为近地无人机集群的自主导航与协同作业提供了高效可靠的解决方案，具备良好的工程实用价值。

This paper addresses the problem of degraded tracking performance in small UAV swarms operating in near-ground envi-ronments due to wind speed disturbances. A hybrid controller scheduling strategy based on wind speed grading is proposed to achieve robustness and control efficiency for the formation system across all operating conditions. First, a wind disturb-ance model is established, decomposing the composite disturbance into a slowly varying mean wind, turbulence components, and unknown bounded disturbances, and the wind intensity is classified based on the Beaufort scale. On this basis, a three-level progressive control architecture is designed: for low wind speeds(Levels 1-2), an optimal control law is developed with-in a reinforcement learning framework to balance energy consumption and tracking accuracy; for moderate wind speeds(Levels 3), a distributed cooperative extended state observer is introduced to improve disturbance estimation accuracy through network collaboration; and for high wind speeds(Levels 4-5), adaptive integral sliding mode compensation is inte-grated to effectively suppress severe disturbances. Based on the leader-follower consensus principle, a unified analytical framework is constructed using Lyapunov theory and input-to-state stability theory, with a hysteresis-based switching strate-gy designed to avoid controller chattering at wind level boundaries. Theoretical analysis proves that the formation states ex-hibit global asymptotic stability and bounded convergence. Simulation experiments demonstrate that the proposed method effectively suppresses disturbances under varying wind speeds and communication topologies, while exhibiting superior performance in energy efficiency, disturbance rejection capability, and dynamic response speed. The approach provides an efficient and reliable solution for autonomous navigation and cooperative operations of near-ground UAV swarms, offering strong practical engineering value.