Hierarchical parameter estimation for distributed networked systems: a dynamic consensus approach
For control and estimation in distributed networked systems, this provides a novel method with guaranteed exponential convergence and flexibility for practical extensions.
This work proposes a two-stage distributed framework for estimating constant parameters in networked systems, achieving exponential convergence of a local gradient estimator by designing a consensus gain that ensures persistence of excitation. The framework supports extensions to switched topologies, quantization, and relaxed excitation requirements via DREM.
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.