通过增广拉格朗日方法学习数据驱动系统的神经反馈线性化
Learning Neural Feedback Linearization for Data-driven Systems via Augmented Lagrangian
查看机构详情
- South Westphalia University of Applied Sciences（南威斯特法伦应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
本文提出一种数据驱动框架，通过将相对阶条件融入学习过程，用神经李导数替代传统组件实现反馈线性化，并建立闭环稳定性条件，在直流电机上验证了有效性。
中文摘要 AI 辅助
本文提出了一种新颖的数据驱动框架，用于设计和训练反馈线性化控制器，其核心在于将基于相对阶的条件显式地纳入学习过程。这使得传统的反馈控制器组件能够被神经李导数所替代，从而构建一个完全数据驱动的反馈线性化框架。此外，通过推导有界辨识误差导致有界跟踪误差的充分条件，建立了实用的闭环稳定性。所推导的理论结果通过其在电枢控制直流电机上的应用得到了验证。
英文摘要
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.
