用自适应神经网络在线分解误差，实时补偿多旋翼运输系统扰动。
Online Identification using Adaptive Laws and Neural Networks for Multi-Quadrotor Centralized Transportation System
- 将高维误差空间分解为低维子空间，分块由自适应律和浅层网络在线学习
- 无需持续激励和离线训练，可实时补偿时变扰动与模型不确定性
- 适用于强耦合非线性系统，适合多旋翼协同运输场景
本文提出一种自适应神经识别方法，以增强集中式多旋翼运输系统的鲁棒性。该方法通过在分解的误差子空间上进行在线调参与学习，实现对作用于载荷的时变扰动和模型不确定性的高效实时补偿。策略是将高维误差空间分解为一组低维子空间，使未见特征的识别问题自然转化为由多个自适应律和浅层神经网络处理的子映射（“切片”），这些子映射通过基于李雅普诺夫的自适应机制在线更新，无需持续激励（PE）和离线训练。由于神经网络的无模型特性，该方法能良好适配高度耦合且非线性的集中式运输系统。它作为前馈补偿器作用于载荷控制器，不显式依赖与载荷耦合的动力学（如缆绳和四旋翼）。所提控制系统已在李雅普诺夫意义下证明稳定，数值仿真验证了其在时变扰动和模型不确定性下的增强鲁棒性。
原文摘要 · Abstract (English)
This paper introduces an adaptive-neuro identification method that enhances the robustness of a centralized multi-quadrotor transportation system. This method leverages online tuning and learning on decomposed error subspaces, enabling efficient real-time compensation to time-varying disturbances and model uncertainties acting on the payload. The strategy is to decompose the high-dimensional error space into a set of low-dimensional subspaces. In this way, the identification problem for unseen features is naturally transformed into submappings (``slices'') addressed by multiple adaptive laws and shallow neural networks, which are updated online via Lyapunov-based adaptation without requiring persistent excitation (PE) and offline training. Due to the model-free nature of neural networks, this approach can be well adapted to highly coupled and nonlinear centralized transportation systems. It serves as a feedforward compensator for the payload controller without explicitly relying on the dynamics coupled with the payload, such as cables and quadrotors. The proposed control system has been proven to be stable in the sense of Lyapunov, and its enhanced robustness under time-varying disturbances and model uncertainties was demonstrated by numerical simulations.
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