🤖 AI Summary
This work addresses the challenge that online updates of neural network controllers can compromise closed-loop stability. To overcome this, the authors propose a stability-preserving online update mechanism that, for the first time, enables stable switching of nonlinear neural controllers. By modeling the controller as an ℓp-gain-bounded causal operator, they derive sufficient gain conditions and design two update strategies—time-scheduled and state-triggered—that decouple stability guarantees from controller optimization, thereby accommodating approximate or early-stopped training. Under time-varying references and disturbances, the method continuously improves control performance while rigorously preserving closed-loop ℓp stability across multiple updates, significantly outperforming both static and naive online baselines.
📝 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 approximate or early-stopped controller synthesis. We demonstrate the approach on nonlinear systems with time-varying objectives and disturbances, and show consistent performance improvements over static and naive online baselines while guaranteeing stability.