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Kalman filter‐based adaptive control for networked systems with unknown parameters and randomly missing outputs
Author(s) -
Shi Y.,
Fang H.,
Yan M.
Publication year - 2009
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.1390
Subject(s) - kalman filter , estimator , control theory (sociology) , autoregressive model , computer science , bernoulli's principle , convergence (economics) , filter (signal processing) , process (computing) , control (management) , mathematics , engineering , artificial intelligence , statistics , aerospace engineering , economic growth , economics , computer vision , operating system
This paper investigates the problem of adaptive control for networked control systems with unknown model parameters and randomly missing outputs. In particular, for a system with the autoregressive model with exogenous input placed in a network environment, the randomly missing output feature is modeled as a Bernoulli process. Then, an output estimator is designed to online estimate the missing output measurements, and further a Kalman filter‐based method is proposed for parameter estimation. Based on the estimated output and the available output, and the estimated model parameters, an adaptive control is designed to make the output track the desired signal. Convergence properties of the proposed algorithms are analyzed in detail. Simulation examples illustrate the effectiveness of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd.