Neural Closed-Loop Identification of Nonlinear Systems with Stability and Generalization Guarantees
Neural networks are expressive models of nonlinear dynamics, but identifying such systems from closed-loop data raises two difficulties: the feedback loop correlates inputs with measurement noise, biasing standard open-loop estimators, and the identified model must be stabilized by the data-collection controller, so that its closed-loop behavior matches the true plant's. We derive a complete parameterization of all nonlinear systems stabilized by a given controller, generalizing the classical dual-Youla parameterization to the nonlinear setting. Building on this characterization, we propose Internal Controller Identification (ICI), a computationally tractable framework for identifying pot...
| Authors | Laura Meroi, Mahrokh G. Boroujeni, Leonardo Massai et al. |
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| Primary category | eess.SY |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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