Approximate Feedback Linearization for a Nonlinear Hyperbolic PDE Class -- Part II: Neural Operator

Miroslav Krstic

Approximate Feedback Linearization for a Nonlinear Hyperbolic PDE Class -- Part II: Neural Operator

Systems and Control 2026-07-05 v1 Optimization and Control

Authors: Miroslav Krstic

Abstract

Volterra series feedback linearizes a class of nonlinear hyperbolic PDEs but produces a controller that, even after truncation, demands solving a tower of plant-specific kernel PDEs and evaluating nested integrals. We prove the truncated controller is jointly Lipschitz in plant and state, and learn it as a single neural operator from plant nonlinearity and state to boundary control. Once trained, no kernel is ever solved again, for any plant in the trained class. The closed loop is practically stable in class- $\mathcal{KL}$ form, with a residual ball scaling linearly with training accuracy.

Cite

@article{arxiv.2607.04362,
  title  = {Approximate Feedback Linearization for a Nonlinear Hyperbolic PDE Class -- Part II: Neural Operator},
  author = {Miroslav Krstic},
  journal= {arXiv preprint arXiv:2607.04362},
  year   = {2026}
}