2020
DOI: 10.1002/acs.3100
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Distributed data‐driven observer for linear time invariant systems

Abstract: Summary This paper is concerned with distributed data‐driven observer design problem. The existing data‐driven observers rely on a common assumption that all the information about the system, and the calculations based upon this information are centralized. Therefore the resulting algorithms cannot be applied to the distributed systems in which each local observer receives only a part of the output signal. On the other hand, traditional model‐based distributed state estimation methods generally assume that the… Show more

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Cited by 5 publications
(3 citation statements)
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“…Remark 4. It is difficult to compute the partial derivative of P(t + 1) in (27) in the case that P(t + 1) is a matrix. Li and Todorov computed the partial derivative of the trace of the covariance matrix with respect to the gain vector for the purpose of computing the optimal filter gain.…”
Section: (T)c]e(t) + Mu(t)e(t) − L(t)v(t)mentioning
confidence: 99%
See 2 more Smart Citations
“…Remark 4. It is difficult to compute the partial derivative of P(t + 1) in (27) in the case that P(t + 1) is a matrix. Li and Todorov computed the partial derivative of the trace of the covariance matrix with respect to the gain vector for the purpose of computing the optimal filter gain.…”
Section: (T)c]e(t) + Mu(t)e(t) − L(t)v(t)mentioning
confidence: 99%
“…Much existing work focuses on the state and parameter estimation problems for linear state-space models. 27 However, the bilinear models have the nonlinear product terms of control variables and state variables, it increases the difficulty of the combined parameter and state estimation. The method in Reference 28 derived a multi-innovation generalized extended stochastic gradient algorithm for only considering the parameter estimation of the bilinear systems by transforming a bilinear model to the input-output representation and eliminating the state variables of the system.…”
Section: Introductionmentioning
confidence: 99%
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