Multi-unmanned aerial vehicle (UAV) cooperative lift systems use multiple UAVs to collectively lift and transport payloads. These systems have unique benefits over standard single-vehicle logistics paradigms in that they distribute lift capacity among several potentially inexpensive and man-portable aircraft, and furthermore provide redundancy to guard against vehicle failure. However, several challenges arise when multiple vehicles are tasked with coordinating stabilization and control tasks for an unknown payload, particularly when there is also uncertainty regarding relative vehicle placement and orientation. This paper presents a novel adaptive control framework for modular cooperative lift systems. The algorithm uses an extended Kalman filter to update system parameters within a control allocation scheme. Thrust vectoring is employed to ensure adequate yaw control authority, and null-space excitation is used to speed estimator convergence. The adaptive control approach is designed to execute a full payload transportation mission starting from rest on the ground. Extensive simulation studies and preliminary flight experiments evaluate robustness of the adaptive control scheme and explore tradeoffs with respect to several system parameters and levels of uncertainty. Overall, the proposed control and estimator scheme is shown to be highly effective in stabilizing multi-UAV payload systems under realistic uncertainty in vehicle configuration and payload parameters.

多无人机（UAV）协同提升系统使用多架无人机共同提升和运输有效载荷。与标准的单车物流范式相比，这些系统具有独特的优势，因为它们可以在几架潜在的廉价和便携式飞机之间分配升力能力，并且还提供冗余以防止车辆故障。然而，当多辆车辆负责协调未知有效载荷的稳定和控制任务时，会出现一些挑战，特别是当相对车辆放置和方向也存在不确定性时。本文提出了一种用于模块化协同升降系统的新型自适应控制框架。该算法使用扩展卡尔曼滤波器来更新控制分配方案内的系统参数。采用推力矢量化以确保足够的偏航控制权限，并使用零空间激励来加速估计器收敛。自适应控制方法旨在执行从地面静止开始的完整有效载荷运输任务。广泛的模拟研究和初步飞行实验评估了自适应控制方案的鲁棒性，并探索了几个系统参数和不确定性水平的权衡。总体而言，在车辆配置和有效载荷参数的现实不确定性下，所提出的控制和估计器方案在稳定多无人机有效载荷系统方面非常有效。自适应控制方法旨在执行从地面静止开始的完整有效载荷运输任务。广泛的模拟研究和初步飞行实验评估了自适应控制方案的鲁棒性，并探索了几个系统参数和不确定性水平的权衡。总体而言，在车辆配置和有效载荷参数的现实不确定性下，所提出的控制和估计器方案在稳定多无人机有效载荷系统方面非常有效。自适应控制方法旨在执行从地面静止开始的完整有效载荷运输任务。广泛的模拟研究和初步飞行实验评估了自适应控制方案的鲁棒性，并探索了几个系统参数和不确定性水平的权衡。总体而言，在车辆配置和有效载荷参数的现实不确定性下，所提出的控制和估计器方案在稳定多无人机有效载荷系统方面非常有效。