Event‐triggered resilient distributed extended Kalman filter with consensus on estimation

Rezaei, Hossein; Ghorbani, Majid

doi:10.1002/rnc.5881

Cited by 15 publications

(8 citation statements)

References 41 publications

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“…The algorithms derived for the state estimation of FOS systems introduced so far in the literature are based on the assumption that the model of the plant is exact. This assumption is not realistic in engineering practice because the models represent approximations of real systems, where the performance of the traditional KF deteriorates considerably 17‐19 . In this regard, three different approaches were applied to standard state‐space estimation to extend the classical KF form to a robust one 20‐24 .…”

Section: Introductionmentioning

confidence: 99%

“…This assumption is not realistic in engineering practice because the models represent approximations of real systems, where the performance of the traditional KF deteriorates considerably. [17][18][19] In this regard, three different approaches were applied to standard state-space estimation to extend the classical KF form to a robust one. [20][21][22][23][24] In case of H-∞, the filter is constructed by mapping the uncertainties into the estimation errors for linear models.…”

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confidence: 99%

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Robust fractional order singular Kalman filter

Nosrati,

Belikov,

Tepljakov

et al. 2023

Intl J Robust & Nonlinear

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In this article, the state estimation problem of linear fractional order singular (FOS) systems subject to matrix uncertainties is investigated where a recursive robust algorithm is derived. Considering an uncertain discrete‐time linear FOS system with added process and measurement noises, we aim to design a robust Kalman‐type state estimation algorithm based on an optimal data fitting approach with a given sequence of observations. As a substitute for the stochastic formulation, this general filter is obtained by minimizing a completely deterministic regularized residual norm in its worst‐possible form at each step over admissible uncertainties. Analysis of the algorithm shows that not only does the proposed robust filter cover the traditional robust Kalman filters (KFs), but it also represents an extension of the nominal fractional singular KF (FSKF) when the system is not subject to uncertainties. Furthermore, besides giving a sufficient condition for the existence of the robust filter, we derive conditions for the asymptotic properties of the filter, where we demonstrate that the filter and the Riccati equation are stable and converge when an equivalent system is detectable and stabilizable. A numerical example is included to demonstrate the performance of the introduced filter.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Robust fractional order singular Kalman filter

Nosrati,

Belikov,

Tepljakov

et al. 2023

Intl J Robust & Nonlinear

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“…The time-triggered solution 18 reduces the communication frequency by choosing fewer transmission moments, which may cause the loss of some necessary information. Alternatively, the event-triggered solution 19,20 only sends data when the predetermined conditions are satisfied. By choosing the data to be sent instead of determining the time sequence of transmission, the communication energy consumption can be reduced on the premise of collecting the necessary information.…”

Section: Introductionmentioning

confidence: 99%

A distributed diffusion Kalman filter with event‐triggered mechanism and guaranteed stability

Chen,

Liu,

Wang

et al. 2023

Intl J Robust & Nonlinear

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In this article, a distributed diffusion Kalman filtering algorithm with event‐triggered communication (DDKF‐E) is studied for discrete‐time nonlinear systems. According to the event‐triggered communication protocol, the data between sensors and estimators are transmitted only when the predefined conditions are satisfied. Considering the characteristic of event‐triggered method and truncated error by linearization, an upper bound of the estimation error covariance matrix is obtained by using the variance‐constrained method. The Kalman gain is designed to minimize the upper bound and then two Riccati equations are obtained. Furthermore, the stochastic stability theory is used to prove the stability of DDKF‐E, and it is derived that the estimation error of DDKF‐E is exponentially bounded in mean square. Finally, numerical simulations validate the effectiveness of the DDKF‐E algorithm.

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“…For instance, deterministic [13] and stochastic [14] event-triggering rules have been used to construct distributed Kalman filters. The proposal in [15] borrows these ideas to construct an extended Kalman filter. Distributed set-membership estimators have also been studied in the context of event-triggered communication [16].…”

Section: Introductionmentioning

confidence: 99%

“…However, most event-triggered and discrete-time approaches for dynamic consensus are based on linear techniques, even in recent works [15]. This means that the estimations cannot be exact when the dynamic average is persistently varying, e.g., when the local signals behave as a sinusoidal.…”

Section: Introductionmentioning

confidence: 99%

Precise Dynamic Consensus under Event-Triggered Communication

2023

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This work addresses the problem of dynamic consensus, which consists of estimating the dynamic average of a set of time-varying signals distributed across a communication network of multiple agents. This problem has many applications in robotics, with formation control and target tracking being some of the most prominent ones. In this work, we propose a consensus algorithm to estimate the dynamic average in a distributed fashion, where discrete sampling and event-triggered communication are adopted to reduce the communication burden. Compared to other linear methods in the state of the art, our proposal can obtain exact convergence under continuous communication even when the dynamic average signal is persistently varying. Contrary to other sliding-mode approaches, our method reduces chattering in the discrete-time setting. The proposal is based on the discretization of established exact dynamic consensus results that use high-order sliding modes. The convergence of the protocol is verified through formal analysis, based on homogeneity properties, as well as through several numerical experiments. Concretely, we numerically show that an advantageous trade-off exists between the maximum steady-state consensus error and the communication rate. As a result, our proposal can outperform other state-of-the-art approaches, even when event-triggered communication is used in our protocol.

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Event‐triggered resilient distributed extended Kalman filter with consensus on estimation

Cited by 15 publications

References 41 publications

Robust fractional order singular Kalman filter

Robust fractional order singular Kalman filter

A distributed diffusion Kalman filter with event‐triggered mechanism and guaranteed stability

Precise Dynamic Consensus under Event-Triggered Communication

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