Stability-Preserving Online Adaptation of Neural Closed-loop Maps
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910066669518848 |
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| author | Saccani, Danilo Furieri, Luca Ferrari-Trecate, Giancarlo |
| author_facet | Saccani, Danilo Furieri, Luca Ferrari-Trecate, Giancarlo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22469 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Stability-Preserving Online Adaptation of Neural Closed-loop Maps Saccani, Danilo Furieri, Luca Ferrari-Trecate, Giancarlo Systems and Control Artificial Intelligence Optimization and Control 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. |
| title | Stability-Preserving Online Adaptation of Neural Closed-loop Maps |
| topic | Systems and Control Artificial Intelligence Optimization and Control |
| url | https://arxiv.org/abs/2603.22469 |