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Learning Neural Feedback Linearization for Data-driven Systems via Augmented Lagrangian

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Computer Science > Machine Learning

arXiv:2609.25163 (cs)
[Submitted on 21 Sep 2026]

Title:Learning Neural Feedback Linearization for Data-driven Systems via Augmented Lagrangian

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Abstract:The paper proposes a novel data-driven framework for designing and training a feedback linearizing controller by explicitly incorporating relative degree based conditions into the learning process. This enables the conventional feedback controller components to be replaced by neural Lie derivatives, thereby facilitating a fully data-driven feedback linearization framework. Furthermore, practical closed-loop stability is established by deriving sufficient conditions under which bounded identification errors lead to bounded tracking errors. The derived theoretical results are validated through their application to an armature controlled DC motor.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2609.25163 [cs.LG]
  (or arXiv:2609.25163v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2609.25163
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Lakshmipriya P.K. [view email]
[v1] Mon, 21 Sep 2026 11:51:04 UTC (1,048 KB)
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