针对无人机编队在参数不确定下的鲁棒控制，提出基于敏感度的管式非线性模型预测控制方法。
Sensitivity-Based Tube NMPC for Cooperative Aerial Structures Under Parametric Uncertainty
- 利用参数敏感度在线计算约束收紧量，增强对质量、长度、惯性的不确定性鲁棒性
- 在蒙特卡洛采样下保持良好跟踪性能，约束满足率优于传统方法
- 适合需要高可靠性的多无人机协同飞行任务，如精确编队与边界贴合机动
本文提出一种基于敏感度的管式非线性模型预测控制（NMPC）框架，用于平面内由刚性连接件组成的双无人机链在有界参数不确定性下的协同控制。系统采用输入速率驱动模型，以施加推力与扭矩的幅值和变化率限制。通过沿控制时域传播一阶参数状态敏感度，实时计算约束收紧裕度，实现对链间距离约束（通过平滑余弦嵌入实现）和推力大小上限的鲁棒化。该方法在MATLAB中实现，并通过贴边机动与蒙特卡洛不确定性采样进行评估。结果表明，在存在参数不确定性时，约束裕度显著提升，同时跟踪性能与无扰动情况下的标准NMPC相当。
原文摘要 · Abstract (English)
This paper presents a sensitivity-based tube Nonlinear Model Predictive Control (NMPC) framework for cooperative aerial chains under bounded parametric uncertainty. We consider a planar two-vehicle chain connected by rigid links, modeled with input-rate actuation to enforce slew-rate and magnitude limits on thrust and torque. Robustness to uncertainty in link mass, length, and inertia is achieved by propagating first-order parametric state sensitivities along the horizon and using them to compute online constraint-tightening margins. We robustify an inter-link separation constraint, implemented via a smooth cosine embedding, and thrust-magnitude bounds. The method is implemented in MATLAB and evaluated with boundary-hugging maneuvers and Monte-Carlo uncertainty sampling. Results show improved constraint margins under uncertainty with tracking performance comparable to nominal NMPC.
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