无传感器估计吊载状态，提升无人机吊运精度。
Sensorless State Estimation and Control for Agile Cable-Suspended Payload Transport by Quadrotors
- 基于几何约束的建模方法，直接整合缆绳张力。
- 实验证明轨迹误差显著降低，性能优于传统方法。
- 适合需要高精度吊运的无人机系统研发者。
本文提出一种新型控制与估计方法，用于无人机吊运缆绳悬挂负载的空中操作。现有方法依赖负载直接测量和拉格朗日建模，但系统缺乏简洁的动力学模型。为此，本文采用Udwadia-Kalaba方法显式引入缆绳几何约束，推导出张力并直接集成至非线性模型预测控制（NMPC）预测模型中。同时，提出基于相同几何约束的无传感器负载状态估计方法。真实机器人实验表明，将负载动力学显式纳入优化问题可显著减少轨迹跟踪误差，整体性能优于基于不完整模型的策略。
原文摘要 · Abstract (English)
This work proposes a novel control and estimation approach for aerial manipulation of a cable-suspended load using Unmanned Aerial Vehicles (UAVs). Common approaches in the state of the art have practical limitations, relying on direct load measurements and Lagrangian methods for dynamic modeling. The lack of a straightforward dynamic model of the system led us to propose adopting the Udwadia-Kalaba method to explicitly incorporate the cable's geometric constraints. This formulation allowed for the consistent derivation of the tension force and its direct integration into the NMPC prediction model. Additionally, we propose a sensorless load state estimation based on the same geometric constraints. Results from real-robot experiments demonstrated that the explicit inclusion of load dynamics in the optimization problem significantly reduces trajectory-tracking errors and yields better overall performance compared to strategies based on incomplete models.
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