三架无人机吊运负载时，能抗干扰精准跟踪路径。
A Robust Neural Control Design for Multi-drone Slung Payload Manipulation with Control Contraction Metrics
- 用控制收缩度量设计神经控制器，保证收敛性且不超执行器极限。
- 结合扰动估计技术，可消除持续干扰，实现零轨迹误差。
- 模块化设计适合多无人机系统，对工程应用友好。
本文针对三架无人机吊挂负载运输系统，在外部干扰下实现参考路径跟踪的鲁棒神经控制设计。采用控制收缩度量（CCM）生成具有指数收敛特性的基线控制器，同时满足控制输入饱和约束。引入不确定性与扰动估计（UDE）技术，动态补偿持续扰动。所提框架为模块化设计，使控制器与估计器各自独立运行，当扰动满足特定假设时，可实现零轨迹跟踪误差。完整系统的稳定性和鲁棒性得到理论证明。仿真验证了该控制设计在外部干扰下跟踪复杂轨迹的能力。
原文摘要 · Abstract (English)
This paper presents a robust neural control design for a three-drone slung payload transportation system to track a reference path under external disturbances. The control contraction metric (CCM) is used to generate a neural exponentially converging baseline controller while complying with control input saturation constraints. We also incorporate the uncertainty and disturbance estimator (UDE) technique to dynamically compensate for persistent disturbances. The proposed framework yields a modularized design, allowing the controller and estimator to perform their individual tasks and achieve a zero trajectory tracking error if the disturbances meet certain assumptions. The stability and robustness of the complete system, incorporating both the CCM controller and the UDE compensator, are presented. Simulations are conducted to demonstrate the capability of the proposed control design to follow complicated trajectories under external disturbances.
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