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Robust input‐output finite‐time filtering for uncertain Markovian jump nonlinear systems with partially known transition probabilities
Author(s) -
Ren Hangli,
Zong Guangdeng
Publication year - 2017
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2777
Subject(s) - nonlinear system , mathematics , control theory (sociology) , markov process , lyapunov function , linear matrix inequality , stochastic matrix , bounded function , jump , markov chain , computer science , mathematical optimization , mathematical analysis , control (management) , statistics , quantum mechanics , artificial intelligence , physics
Summary In this paper, the robust input‐output finite‐time filtering problem is addressed for a class of uncertain Markovian jump nonlinear systems with partially known transition probabilities. Here, the disturbances, uncertainties, state delay, and distributed delays are all taken into account. Both the stochastic finite‐time boundedness and the stochastic input‐output finite‐time stability are introduced to the Markovian jump nonlinear systems with partially known transition probabilities. By constructing a reasonable stochastic Lyapunov functional and using linear matrix inequality techniques, sufficient conditions are established to guarantee the filtering error systems are stochastic finite‐time bounded and stochastic input‐output finite‐time stable, respectively. Finally, 2 examples are provided to illustrate the effectiveness of the proposed methods.