柔性着陆器主体由柔性材料构造，能够有效降低其在弱引力环境附着时发生倾覆、反弹的风险。然而，柔性体带来的强非线性动力学与多节点协同需求，也增加了制导设计难度。针对柔性附着协同制导问题，提出了一种自适应智能制导方法。首先，构建了简化柔性附着轨迹规划模型，在保留系统主要运动特征的基础上，实现了参考轨迹的高效生成。进一步，证明了柔性着陆器可对参考轨迹实现稳定跟踪，并据此设计了强化学习辅助的制导律结构。在该结构下，强化学习仅用于在线参数自适应调节与补偿输入生成，从而有效降低了策略学习难度。通过在奖励函数中综合考虑附着平稳性与约束满足等性能指标，使所得策略在跟踪参考轨迹的同时，进一步改善附着平稳性并降低燃料消耗。最后，基于小天体433Eros模型进行柔性附着协同制导仿真，检验了所提自适应制导方法的有效性。

A flexible lander constructed with compliant material can effectively reduce the risks of overturning and rebound during landing in weak-gravity environments. However, the strong nonlinear dynamics introduced by the flexible structure and the requirement for multi-node cooperation increase the difficulty of guidance design. To address the cooperative guidance problem for flexible landing, an adaptive intelligent guidance method is proposed. First, a simplified trajectory planning model for flexible landing is developed, enabling efficient generation of reference trajectories while preserving the primary motion characteristics of the system. Furthermore, the stable trackability of the reference trajectory by the flexible lander is established, based on which a reinforcement learning-assisted guidance law is constructed. Within this framework, reinforcement learning is only used for online parameter adaptation and compensating-input generation, thereby reducing the learning complexity. By incorporating performance indices such as attitude regulation and constraint satisfaction into the reward function, the learned policy is able to improve landing steadiness and reduce fuel consumption while maintaining accurate tracking of the reference trajectory. Finally, cooperative guidance simulations for flexible landing are conducted based on the small celestial body 433 Eros, demonstrating the effectiveness of the proposed adaptive guidance method.