SOURCE-LINKED INTELLIGENCE
Learning Neural Feedback Linearization for Data-driven Systems via Augmented Lagrangian
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 t
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T11:51:04.000Z
First collected: 2026-09-23T08:01:43.213Z. This is not the publication date.