Lipschitz Youla

React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN

arXiv:2506.01226v3 Announce Type: replace Abstract: We study parameterizations of stabilizing nonlinear policies for learning-based control. We propose a structure based on a nonlinear version of the Youla-Kucera parameterization combined with robust neural networks such as the recurrent equilibrium network (REN). The resulting parameterizations are unconstrained, and hence can be searched over with first-order optimization methods, while always ensuring closed-loop stability by construction.