A dual-pathway Neural-ESO uses a Lipschitz-bounded neural network for feedforward disturbance prediction and a conventional ESO for online correction, guaranteeing uniform ultimate boundedness of the closed-loop system.
From PID to active disturbance rejection control,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2years
2026 2representative citing papers
Robust OSCTC framework using ESO for disturbance estimation in task space, robust CBF for safety, and sliding-window conformal prediction for online disturbance bound estimation, demonstrated on 7-DoF Franka arm with mm tracking at 1 kHz.
citing papers explorer
-
Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control
A dual-pathway Neural-ESO uses a Lipschitz-bounded neural network for feedforward disturbance prediction and a conventional ESO for online correction, guaranteeing uniform ultimate boundedness of the closed-loop system.
-
Robust Operational Space Control with Conformal Disturbance Bounds for Safe Redundant Manipulation
Robust OSCTC framework using ESO for disturbance estimation in task space, robust CBF for safety, and sliding-window conformal prediction for online disturbance bound estimation, demonstrated on 7-DoF Franka arm with mm tracking at 1 kHz.