Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control

2026-07-07

AI Summary

This work addresses the limited robustness of learning-based control under strong disturbances and out-of-distribution scenarios, where overreliance on learned models often leads to performance degradation. To overcome this, the authors propose Neural-ESO, a dual-path architecture that combines a neural network-based feedforward predictor for rapid disturbance estimation with an extended state observer (ESO) that corrects prediction errors, thereby reducing dependence on the learning component. By innovatively integrating neural networks with ESO and incorporating Lipschitz continuity constraints, the method establishes, for the first time, uniform ultimate boundedness of the closed-loop error dynamics, offering theoretically guaranteed robustness. Evaluated on a quadrotor landing task under strong ground effect, Neural-ESO consistently outperforms state-of-the-art baselines across training, deployment, and transfer phases, achieving a superior balance between accuracy and reliability.

Abstract

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.

Problem

• learning-based control
• disturbance rejection
• robustness
• model reliability
• out-of-distribution generalization

Innovation

• Neural Extended State Observer
• dual-pathway architecture
• provably robust control
• Lipschitz-bounded learning
• disturbance rejection