多旋翼协同运输系统在线自适应控制，提升抗干扰能力
Robustness Enhancement for Multi-Quadrotor Centralized Transportation System via Online Tuning and Learning
- 通过神经网络与自适应律实时调整模型参数和扰动估计
- 无需预训练或持续激励条件，保证误差有界且系统稳定
- 适合复杂环境下的高鲁棒性多机协同任务
本文提出一种自适应神经几何控制方法，用于中心化多旋翼协同运输系统，以增强系统的自适应性和抗干扰能力。通过在现有几何控制基础上引入多个神经网络与自适应律，实现模型参数与神经网络权重的在线协同调节。基于李雅普诺夫理论的自适应机制确保了估计误差有界，无需预训练或持续激励（PE）条件。所提控制方案在特定前提下被证明具有李雅普诺夫稳定性，并通过数值仿真验证了其在扰动环境及模型不匹配工况下的增强鲁棒性。
原文摘要 · Abstract (English)
This paper introduces an adaptive-neuro geometric control for a centralized multi-quadrotor cooperative transportation system, which enhances both adaptivity and disturbance rejection. Our strategy is to coactively tune the model parameters and learn the external disturbances in real-time. To realize this, we augmented the existing geometric control with multiple neural networks and adaptive laws, where the estimated model parameters and the weights of the neural networks are simultaneously tuned and adjusted online. The Lyapunov-based adaptation guarantees bounded estimation errors without requiring either pre-training or the persistent excitation (PE) condition. The proposed control system has been proven to be stable in the sense of Lyapunov under certain preconditions, and its enhanced robustness under scenarios of disturbed environment and model-unmatched plant was demonstrated by numerical simulations.
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