Title: Online Identification using Adaptive Laws and Neural Networks for Multi-Quadrotor Centralized Transportation System Show Chinese title

Title: 基于自适应律和神经网络的多旋翼机集中式运输系统的在线辨识

Abstract: 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. Show Chinese abstract

Abstract: 本文介绍了一种自适应神经识别方法，该方法增强了集中式多旋翼机运输系统的鲁棒性。 该方法利用分解误差子空间上的在线调整和学习，能够对作用在负载上的时变扰动和模型不确定性进行高效的实时补偿。 该策略是将高维误差空间分解为一组低维子空间。 通过这种方式，对于未见过特征的识别问题自然地转化为由多个自适应定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在线调整进行更新，而无需依赖持续激励（PE）和离线训练。 由于神经网络的无模型特性，这种方法可以很好地适应高度耦合和非线性的集中式运输系统。 它作为负载控制器的前馈补偿器，而不显式依赖于与负载耦合的动力学，例如电缆和旋翼机。 所提出的控制系统已被证明在李雅普诺夫意义上是稳定的，并且通过数值仿真展示了其在时变扰动和模型不确定性下的增强鲁棒性。