Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control
It is shown that enforcing a Lipschitz bound on the learning component guarantees uniform ultimate boundedness of the closed-loop error dynamics, demonstrating accuracy-robustness trade-off and greater operational reliability during training, deployment, and transfer compared with state-of-the-art baselines.