用学习方法优化不确定系统的观测器，提升状态估计精度。
Learning-Enhanced Observer for Linear Time-Invariant Systems with Parametric Uncertainty
- 将系统矩阵设为可优化变量，通过梯度下降最小化输出误差
- 在多种系统规模下，估计误差降低超15%，效果显著且稳定
- 适合需要高精度状态估计的工程系统，如自动驾驶、机器人控制
本文提出一种面向参数不确定线性时不变系统的学习增强观测器（LEO）。该框架不依赖于理想模型，而是将系统矩阵视为可优化变量，通过梯度下降最小化稳态输出偏差损失来迭代更新。由此获得的数据驱动代理模型，可构造出更优的观测器，有效补偿中等程度的参数不确定性，同时保持经典设计结构。在多组不同维度系统上的蒙特卡洛实验表明，开环与Luenberger观测器的归一化估计误差普遍降低超过15%，且具有系统性和统计显著性。结果表明，现代学习机制可成为传统观测器设计的强大补充，在不确定系统中实现更准确、更鲁棒的状态估计。代码已开源：https://github.com/Hao-B-Shu/LTI_LEO。
原文摘要 · Abstract (English)
This work introduces a learning-enhanced observer (LEO) for linear time-invariant systems with uncertain dynamics. Rather than relying solely on nominal models, the proposed framework treats the system matrices as optimizable variables and refines them through gradient-based minimization of a steady-state output discrepancy loss. The resulting data-informed surrogate model enables the construction of an improved observer that effectively compensates for moderate parameter uncertainty while preserving the structure of classical designs. Extensive Monte Carlo studies across diverse system dimensions show systematic and statistically significant reductions, typically exceeding 15\%, in normalized estimation error for both open-loop and Luenberger observers. These results demonstrate that modern learning mechanisms can serve as a powerful complement to traditional observer design, yielding more accurate and robust state estimation in uncertain systems. Codes are available at https://github.com/Hao-B-Shu/LTI_LEO.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。