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State Estimation of Stochastic Impulsive System Via Stochastic Adaptive Impulsive Observer
Author(s) -
Ayati Moosa,
Alwan Mohamad,
Liu Xinzhi,
Khaloozadeh Hamid
Publication year - 2016
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1151
Subject(s) - observer (physics) , control theory (sociology) , nonlinear system , parametric statistics , state (computer science) , state observer , mathematics , stochastic process , estimation , computer science , engineering , control (management) , algorithm , statistics , artificial intelligence , physics , systems engineering , quantum mechanics
This paper develops stochastic adaptive impulsive observer (SAIO) for state estimation of stochastic impulsive systems. Proposed observer is applicable to linear and a class of nonlinear stochastic impulsive systems. In addition to stochastic noises, the observer considers effect of parametric uncertainty and estimates unknown parameters by suitable adaptation laws. Interestingly, for certain impulsive systems, SAIO gives continuous state estimations from a discrete sequence of system output measurements. New theorems related to stochastic impulsive systems' boundedness are also developed and utilized to prove the boundedness of SAIO state estimation errors. Presented simulation results illustrate the effectiveness of the observer.