Title: Learning Robust State Observers using Neural ODEs (longer version) Show Chinese title

Title: 使用神经ODE学习鲁棒状态观测器（更长版本）

Abstract: 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. Show Chinese abstract

Abstract: 基于神经ODE的最新研究结果，本文提出了一种基于神经ODE的状态观测器设计方法，学习类似于Luenberger的观测器及其非线性扩展（Kazantzis-Kravaris-Luenberger（KKL）观测器），分别用于具有部分已知非线性动态和完全未知非线性动态的系统。特别地，对于可调的KKL观测器，分析了观测器设计与其收敛速度和鲁棒性之间的权衡关系，并以此作为在训练中提高基于学习的观测器鲁棒性的基础。我们通过数值仿真展示了这种方法的优势。