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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1007/s40278-016-15013-9

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1433-3058

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Robust state estimator design for uncertain linear

Authors: Mansour Sheikhan Mohammad Mahdi Bagheri
Publish Date: 2012/07/26
Volume: 23, Issue: 5, Pages: 1395-1406
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Abstract

The estimation of state variables of dynamic systems in noisy environments has been an active research field in recent decades In this way Kalman filtering approach may not be robust in the presence of modeling uncertainties So several methods have been proposed to design robust estimators for the systems with uncertain parameters In this paper an optimized filter is proposed for this problem considering an uncertain discretetime linear system After converting the subject to an optimization problem three algorithms are used for optimizing the state estimator parameters particle swarm optimization PSO algorithm modified genetic algorithm MGA and learning automata LA Experimental results show that in comparison with the standard Kalman filter and some related researches using the proposed optimization methods results in robust performance in the presence of uncertainties However MGAbased estimation method shows better performance in the range of uncertain parameter than other optimization methods

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