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.

翻译：本文提出了一种基于灵敏度的管式非线性模型预测控制（NMPC）框架，用于处理有界参数不确定下的协同空中链式系统。我们考虑一个由刚性连杆连接的两飞行器平面链，采用输入速率激励模型以限制推力和力矩的转换速率及幅值。通过沿预测时域传播一阶参数状态灵敏度，并利用其在线计算约束收紧裕度，实现了对连杆质量、长度及惯量不确定性的鲁棒性。我们强化了通过光滑余弦嵌入实现的链间分离约束，以及推力幅值边界。该方法在MATLAB中实现，并通过边界贴近机动与蒙特卡洛不确定采样进行验证。结果表明，在不确定性下约束裕度得到改善，跟踪性能与标称NMPC相当。