A distributed observer for a time-invariant linear system

Wang, L.; Morse, A. Stephen

doi:10.23919/acc.2017.7963249

Cited by 40 publications

(61 citation statements)

References 12 publications

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“…Next, we include a definition and a necessary assumption for the solvability of Problem 4. [7] and [24]), then Assumption 6 holds true. However, Assumption 6 is in general less restrictive, as it does not enforce connectivity of the network (see for instance the example in Figure 1).…”

Section: Definitionmentioning

confidence: 99%

Distributed estimation based on multi-hop subspace decomposition

et al. 2019

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This paper deals with the problem of distributedly estimating the state of an LTI plant through an interconnected network of agents. The proposed approach results in an observer structure that incorporates consensus among the agents and that can be distributedly designed, achieving a robust solution with a good estimation performance. The developed solution is based on an iterative decomposition of the plant in the local observable staircase forms. The proposed observer has several positive features compared to recent results in the literature, which include milder assumptions on the network connectivity and the ability to set the convergence rate.

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Section: Definitionmentioning

confidence: 99%

Distributed estimation based on multi-hop subspace decomposition

et al. 2019

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“…This is in contrast to [11,33,10,24,37], where only minimum variance solution is studied, and from [34], where the consensus parameters minimize the steady-state mean-square prediction error. In [26,35,29] distributed observers are designed for the case where the state is only partially observable by each sensor, but the estimation weights are designed to guarantee convergence and state omniscience properties, not optimizing bias and error variance features. In [21], the considered problem for distributed estimation is similar, dealing with the design of the consensus parameters and local innovation gains to optimize a different performance criterion.…”

Section: State Of the Artmentioning

confidence: 99%

“…We observe that the KKT conditions hold by imposing ξ i2 = 0 only if λ i is null because of Eq. (35) and therefore ξ i1 = 0 because of Eq. (34).…”

Section: Propositionmentioning

confidence: 99%

Distributed Pareto-optimal state estimation using sensor networks

et al. 2018

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A novel model-based dynamic distributed state estimator is proposed using sensor networks. The estimator consists of a filtering step-which uses a weighted combination of information provided by the sensors-and a model-based predictor of the system's state. The filtering weights and the model-based prediction parameters jointly minimize-at each time-stepthe bias and the variance of the prediction error in a Pareto optimization framework. The simultaneous distributed design of the filtering weights and of the model-based prediction parameters is considered, differently from what is normally done in the literature. It is assumed that the weights of the filtering step are in general unequal for the different state components, unlike existing consensus-based approaches. The state, the measurements, and the noise components are allowed to be individually correlated, but no probability distribution knowledge is assumed for the noise variables. Each sensor can measure only a subset of the state variables. The convergence properties of the mean and of the variance of the prediction error are demonstrated, and they hold both for the global and the local estimation errors at any network node. Simulation results illustrate the performance of the proposed method, obtaining better results than state of the art distributed estimation approaches.

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“…While they vary in their aims, as for example being robust with respect to disturbances as in [8], a key characteristic of an observer is the convergence rate of the error. This has only recently been addressed in [9], where a design was proposed yielding a H. Silm and J.-P. Richard are with Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189 -CRIStAL -Centre de recherche en Informatique Signal et Automatique de Lille, F-59000 Lille, France (e-mail: haik.silm@inria.fr; jean-pierre.richard@centralelille.fr).…”

Section: Introductionmentioning

confidence: 99%

A Note on Distributed Finite-Time Observers

Silm

Ushirobira

Efimov

et al. 2019

IEEE Trans. Automat. Contr.

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The robust distributed estimation for a class of time-invariant plants is achieved via a finite-time observer, its error reaching zero after a finite time in the absence of perturbation. Two types of robustness are also shown. First, input-to-state stability with respect to measurement noises and additive perturbations is proven. Second, we demonstrate that the estimation error stays bounded in the presence of known transmission delays.

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A distributed observer for a time-invariant linear system

Cited by 40 publications

References 12 publications

Distributed estimation based on multi-hop subspace decomposition

Distributed estimation based on multi-hop subspace decomposition

Distributed Pareto-optimal state estimation using sensor networks

A Note on Distributed Finite-Time Observers

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