Tag
The paper proposes a data-driven framework for feedback linearization control using neural Lie derivatives and augmented Lagrangian, with theoretical stability guarantees validated on a DC motor system.
This paper investigates why defeasible priors fail in augmented Lagrangian causal discovery methods, identifying key issues with penalty mechanisms and objective functions, and proposes partial fixes with limited effectiveness.