Stability-Preserving Online Adaptation of Neural Closed-loop Maps
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...
| Authors | Danilo Saccani, Luca Furieri, Giancarlo Ferrari-Trecate |
|---|---|
| Primary category | eess.SY |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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