Problem
Research questions and friction points this paper is trying to address.
data-driven
feedback linearization
closed-loop stability
Learning Neural Feedback Linearization for Data-driven Systems via Augmented Lagrangian
🤖 AI Summary
本文提出一种数据驱动框架，通过将相对度条件纳入学习过程来设计和训练反馈线性化控制器，使用神经李导数替代传统组件，并确保闭环稳定性。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
feedback linearization
data-driven control
neural Lie derivatives
closed-loop stability
🔎 Similar Papers
No similar papers found.
