Physics-Informed Neural Networks for Nonlinear Output Regulation

arXiv:2511.13595v3 Announce Type: replace-cross Abstract: This work addresses the full-information output regulation problem for nonlinear systems, assuming the states of both the plant and the exosystem are known. In this setting, perfect tracking or rejection is achieved by constructing a zero-regulation-error manifold $\pi(w)$ and a feedforward input $c(w)$ that render such manifold invariant. The pair $(\pi(w), c(w))$ is characterized by the regulator equations, i.e., a system of PDEs with a