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Article Dans Une Revue International Journal of Control Année : 2019

A Distributed Kalman Filter with Symbolic Zonotopes and Unique Symbols Provider for Robust State Estimation in CPS

Résumé

Robust state estimation is addressed in a noisy environment and within a distributed and networked architecture. Both bounded disturbances and random noises are considered. A Distributed Zonotopic and Gaussian Kalman Filter (DZG-KF) is proposed where each network node implements a local state estimator using symbolic Zonotopes and Gaussian noise Mergers (s-ZGM), a class of Set-membership and Probabilistic Mergers (SPM). Each network node communicates its own state information only to its neighbours. The proposed system includes a dedicated service called Unique Symbols Provider (USP) giving unique identifiers. It also includes Matrices with Labelled Columns (MLC) featuring column-wise sparsity, and symbolic zonotopes (s-zonotopes). This significantly enhances the propagation of uncertainties and preserves global dependencies that would otherwise be lost (or impeded) by the peer-to-peer communication through the network. A number of other network-related constraints can be managed within this framework. Numerical simulations show significant improvements compared to a non-symbolic approach.
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Dates et versions

hal-02417873 , version 1 (18-12-2019)

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Christophe Combastel, Ali Zolghadri. A Distributed Kalman Filter with Symbolic Zonotopes and Unique Symbols Provider for Robust State Estimation in CPS. International Journal of Control, In press, ⟨10.1080/00207179.2019.1707278⟩. ⟨hal-02417873⟩
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