Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/11696
Title: Joint state and parameter robust estimation of stochastic nonlinear systems
Authors: Stojanović, Vladimir
Nedić, Novak
Journal: International Journal of Robust and Nonlinear Control
Issue Date: 25-Sep-2016
Abstract: Copyright © 2015 John Wiley & Sons, Ltd. Successful implementation of many control strategies is mainly based on accurate knowledge of the system and its parameters. Besides the stochastic nature of the systems, nonlinearity is one more feature that may be found in almost all physical systems. The application of extended Kalman filter for the joint state and parameter estimation of stochastic nonlinear systems is well known and widely spread. It is a known fact that in measurements, there are inconsistent observations with the largest part of population of observations (outliers). The presence of outliers can significantly reduce the efficiency of linear estimation algorithms derived on the assumptions that observations have Gaussian distributions. Hence, synthesis of robust algorithms is very important. Because of increased practical value in robust filtering as well as the rate of convergence, the modified extended Masreliez–Martin filter presents the natural frame for realization of the joint state and parameter estimator of nonlinear stochastic systems. The strong consistency is proved using the methodology of an associated ODE system. The behaviour of the new approach to joint estimation of states and unknown parameters of nonlinear systems in the case when measurements have non-Gaussian distributions is illustrated by intensive simulations. Copyright © 2015 John Wiley & Sons, Ltd.
URI: https://scidar.kg.ac.rs/handle/123456789/11696
Type: journal article
DOI: 10.1002/rnc.3490
ISSN: 10498923
SCOPUS: 84979986951
Appears in Collections:Faculty of Mechanical and Civil Engineering, Kraljevo

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