Hierarchical parameter estimation for distributed networked systems: a dynamic consensus approach
Abstract
This work introduces a novel two-stage distributed framework to globally estimate constant parameters in a networked system, separating shared information from local estimation. The first stage uses dynamic average consensus to aggregate agents' measurements into surrogates of centralized data. Using these surrogates, the second stage implements a local estimator to determine the parameters. By designing an appropriate consensus gain, the persistence of excitation of the regressor matrix is achieved, and thus, exponential convergence of a local Gradient Estimator (GE) is guaranteed. The framework facilitates its extension to switched network topologies, quantization, and the heterogeneous substitution of the GE with a Dynamic Regressor Extension and Mixing (DREM) estimator, which supports relaxed excitation requirements.
Cite
@article{arxiv.2602.14765,
title = {Hierarchical parameter estimation for distributed networked systems: a dynamic consensus approach},
author = {Ariana R. Mendez-Castillo and Rodrigo Aldana-Lopez and Antonio Ramirez-Trevino and Rosario Aragues and David Gomez-Gutierrez},
journal= {arXiv preprint arXiv:2602.14765},
year = {2026}
}