AI RESEARCH
React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN
arXiv CS.LG
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ArXi:2506.01226v3 Announce Type: replace-cross 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.