Learning Provable Neural Network Observer for Uncertain Dynamical Systems
In many safety-critical applications, control of uncertain dynamical systems relies on observers that estimate states and external disturbances. Neural network observers can improve estimation accuracy, but certifying their Lyapunov stability via Linear Matrix Inequality (LMI) constraints leads to large-scale semidefinite programs (SDPs) that are difficult to solve for large networks. To overcome this scalability bottleneck, we propose a novel two-stage training framework for provably stable neural network observers. Our approach decouples the optimization into a point-guided Lyapunov pre-training phase, which rapidly achieves high estimation accuracy and local stability over sampled stat...
| Authors | Zhangyi Wang, Jiaxu Liu, Song Chen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 25 September 2026 |
| Last updated at source | 14 October 2026 |
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