[2603.22469] Stability-Preserving Online Adaptation of Neural Closed-loop Maps

Electrical Engineering and Systems Science > Systems and Control arXiv:2603.22469 (eess) [Submitted on 23 Mar 2026] Title:Stability-Preserving Online Adaptation of Neural Closed-loop Maps Authors:Danilo Saccani, Luca Furieri, Giancarlo Ferrari-Trecate View a PDF of the paper titled Stability-Preserving Online Adaptation of Neural Closed-loop Maps, by Danilo Saccani and 2 other authors View PDF HTML (experimental) Abstract:The growing complexity of modern control tasks calls for controllers that can react online as objectives and disturbances change, while preserving closed-loop stability. Recent approaches for improving the performance of nonlinear systems while preserving closed-loop stability rely on time-invariant recurrent neural-network controllers, but offer no principled way to update the controller during operation. Most importantly, switching from one stabilizing policy to another can itself destabilize the closed-loop. We address this problem by introducing a stability-preserving update mechanism for nonlinear, neural-network-based controllers. Each controller is modeled as a causal operator with bounded $\ell_p$-gain, and we derive gain-based conditions under which the controller may be updated online. These conditions yield two practical update schemes, time-scheduled and state-triggered, that guarantee the closed-loop remains $\ell_p$-stable after any number of updates. Our analysis further shows that stability is decoupled from controller optimality, allowing...