Distributed Adaptive Neural Interval Observers for Unknown Nonlinear Systems
This work develops a distributed observer for unknown nonlinear systems where measurements are incomplete. It uses adaptive neural models to estimate states and weights, ensuring that estimation errors and weights remain bounded. A key achievement is preserving componentwise interval properties across nodes using a cooperative network structure.