Neural-ESO: 証明可能なロバスト学習ベース制御のための二経路アーキテクチャ
Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control
ニューラルネットワークによる予測経路と従来の拡張状態オブザーバによる補正経路を組み合わせた外乱抑制フレームワークを提案し、リプシッツ制約により閉ループ誤差の一様有界性を保証。クアッドローター着陸タスクで有効性を検証した。
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著者: Fan Zhang, Richie Suganda, Jinfeng Chen, Wenhua Liu, Hantao Fu, Bin Hu, Qin Lin
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
