Direct Data-Driven Approximate Optimal Control of Nonlinear Input-Affine Systems
arXiv:2606.10810
Abstract
In this paper, we combine a data-driven system representation with a framework to systematically construct (approximate) solutions to nonlinear optimal control problems. By immersing the unknown dynamics into an extended state space, solutions are characterised via purely data-dependent algebraic conditions. This allows us to design dynamic state-feedback controllers with local stability and performance guarantees for unknown nonlinear, input-affine systems directly using data, without explicitly identifying the dynamics.
This work has been accepted to IFAC World Congress 2026 for publication under a Creative Commons Licence CC-BY-NC-ND