Data-Driven Unknown-Input Observers and State Estimation
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We study distributed least-mean square (LMS) estimation problems over adaptive networks, where nodes cooperatively work to estimate and track common parameters of an unknown system. We consider a scenario where the input and output response signals of the ...
This paper proposes a state estimator for large-scale linear systems described by the interaction of state-coupled subsystems affected by bounded disturbances. We equip each subsystem with a Local State Estimator (LSE) for the reconstruction of the subsyst ...
This paper proposes a state estimator for large-scale linear systems described by the interaction of state-coupled subsystems affected by bounded disturbances. We equip each subsystem with a Local State Estimator (LSE) for the reconstruction of the subsyst ...
In this paper, we consider model combination methods for adaptive filtering that perform unbiased estimation. In this widely studied framework, two adaptive filters are run in parallel, each producing unbiased estimates of an underlying linear model. The o ...
An anisotropic a posteriori error estimate is derived for a finite element discretization of the wave equation in two space dimensions. Only the error due to space discretization is considered, and the error estimates are derived in the nonnatural L-2(0, T ...
This paper presents a novel distributed estimation algorithm based on the concept of moving horizon estimation. Under weak observability conditions we prove convergence of the state estimates computed by any sensor to the correct state even when constraint ...
The paper describes the development and the performance assessment of a Hardware-in-the-Loop (HIL) test platform built as a proof-of-concept of a sub-second State Estimator (SE) of Active Distribution Networks (ADNs). The SE relies on the availability of d ...
Cigré - Conseil International des Grands Réseaux Électriques2013
We propose a partition-based state estimator for linear discrete-time systems composed by coupled subsystems affected by bounded disturbances. The architecture is distributed in the sense that each subsystem is equipped with a local state estimator that ex ...
Integral observers are useful tools for estimating the plant states in the presence of non-vanishing disturbances resulting from plant-model mismatch and exogenous disturbances. It is well known that these observers can eliminate bias in all states, given ...
This paper presents a novel distributed estimation algorithm based on the concept of moving horizon estimation. Under weak observability conditions we prove convergence of the state estimates computed by any sensor to the correct state even when constraint ...
