Event-Based Robust State and Fault Estimation for Stochastic Linear System with Missing Observations and Uncertainty

Author(s):  
Zhidong Xu ◽  
Bo Ding ◽  
Tianping Zhang
Author(s):  
I. Rozora

The problem of estimation of a stochastic linear system has been a matter of active research for the last years. One of the simplest models considers a ‘black box’ with some input and a certain output. The input may be single or multiple and there is the same choice for the output. This generates a great amount of models that can be considered. The sphere of applications of these models is very extensive, ranging from signal processing and automatic control to econometrics (errors-in-variables models). In this paper a time-invariant continuous linear system is considered with a real-valued impulse response function. We assume that impulse function is square-integrable. Input signal is supposed to be Gaussian stationary stochastic process with known spectral density. A sample input–output cross-correlogram is taken as an estimator of the response function. An upper bound for the tail of the distribution of the estimation error is found that gives a convergence rate of estimator to impulse response function in the space Lp(T).


2010 ◽  
Vol 55 (10) ◽  
pp. 2414-2418 ◽  
Author(s):  
Federico Ramponi ◽  
Debasish Chatterjee ◽  
Andreas Milias-Argeitis ◽  
Peter Hokayem ◽  
John Lygeros

2002 ◽  
Vol 35 (1) ◽  
pp. 407-411
Author(s):  
P.-A. Bliman ◽  
A.B. Piunovskiy ◽  
M. Sorine

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