用双路径结构让神经控制更稳，抗干扰能力更强。
Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control
- 双路径设计：神经网络预估扰动，传统观测器纠错
- 约束神经组件的Lipschitz值，确保系统稳定
- 在四轴飞行器着陆中表现更可靠，跨场景适应好
本文提出一种基于神经扩展状态观测器（Neural-ESO）的学习型抗扰框架。与现有方法不同，Neural-ESO采用双路径架构：预测路径利用神经网络提供前馈扰动估计以加速收敛，校正路径则通过传统扩展状态观测器补偿预测误差，防止对神经组件过度依赖。基于李雅普诺夫理论和小增益分析，证明对学习组件施加Lipschitz约束可保证闭环误差动态的统一最终有界性。该框架在受强地面效应扰动的四轴飞行器着陆任务中验证，涵盖正常与分布外场景，展现出精度-鲁棒性权衡，并在训练、部署及迁移过程中优于当前先进基线方法。
原文摘要 · Abstract (English)
A learning-enabled disturbance-rejection framework based on a Neural Extended State Observer (Neural-ESO) is presented in this letter. Unlike existing learning-based control methods that largely rely on the learned model once deployed, Neural-ESO adopts a dual-pathway architecture: a predictive pathway uses a neural network to provide a feedforward disturbance estimate that accelerates convergence, while a corrective pathway employs a conventional ESO to compensate prediction errors and prevent over-reliance on the neural component. Using Lyapunov theory and a small-gain analysis, we show that enforcing a Lipschitz bound on the learning component guarantees uniform ultimate boundedness of the closed-loop error dynamics. The proposed framework is validated on a quadrotor landing task subject to strong ground-effect disturbances across normal and out-of-distribution scenarios, demonstrating accuracy-robustness trade-off and greater operational reliability during training, deployment, and transfer compared with state-of-the-art baselines.
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