Geometry Induced Contraction Degradation and Stabilization of Learning Enabled Observers
Learned measurement models in observers can cause stability issues because their geometry affects error contraction margins. The research shows that high measurement sensitivity can shrink these margins below a critical point, eliminating guaranteed convergence under fixed gains. A new normalization technique restores uniform contraction bounds without retraining the learned model, ensuring robust performance even with varying real-world conditions.