论文标题
使用神经ODE学习强大的状态观察者（更长的版本）
Learning Robust State Observers using Neural ODEs (longer version)
论文作者
论文摘要
本文依靠对神经ODE的最新研究结果，为基于神经od的非线性系统设计的国家观察者提供了一种方法，学习了Luenberger型观察者及其非线性扩展（Kazantzis-kravaris-kravaris-luenberger（KKL）观察者（KKL）观察者，用于具有部分非线性非线性动态的系统的系统。特别是，对于可调的KKL观察者，分析了观察者的设计与融合速度和鲁棒性之间的权衡之间的关系，并将其用作改善基于学习的观察者在训练中的鲁棒性的基础。我们在数值模拟中说明了这种方法的优势。
Relying on recent research results on Neural ODEs, this paper presents a methodology for the design of state observers for nonlinear systems based on Neural ODEs, learning Luenberger-like observers and their nonlinear extension (Kazantzis-Kravaris-Luenberger (KKL) observers) for systems with partially-known nonlinear dynamics and fully unknown nonlinear dynamics, respectively. In particular, for tuneable KKL observers, the relationship between the design of the observer and its trade-off between convergence speed and robustness is analysed and used as a basis for improving the robustness of the learning-based observer in training. We illustrate the advantages of this approach in numerical simulations.
