Analysis And Filtering Of Nonlinear Markov Jump Systems With Non-ideal Measurement

In practical industrial systems,Markov jump systems as a special class of stochastic systems,have attracted increasing attention.Due to the fact that the devices aging,failure or external environmental factors influence the structure or parameters of the systems,it is very practical to use Markov jump systems to describe above systems.In addition,missing measurements and nonlinear perturbations are inevitable in many industrial systems,which can result in the oscillations and instability of the systems.So,it is very important to analyze the Markov jump systems with missing measurements and nonlinear perturbations.Based on the above,this paper focuses on the problem of analysis and filtering for nonlinear Markov jump systems with non-ideal measurement.The main contents of this paper are summarized as followings:In the first part,the problem of boundedness and passivity for nonlinear delay Markov jump systems with missing measurements are considered.Based on Lyapunov-like method,the sufficient condition is got to guarantee the nonlinear systems to be mean-square finite-time bounded.Then,the mean-square finite-time passivity for the system with missing measurements is presented.Finally,a numerical example is provided to demonstrate the proposed method.In the second part,the problem of boundedness and passivity for Markov jump neural networks with randomly distributed delay and sensor nonlinearities are discussed.Based on Lyapunov-like method,the sufficient condition is founded to guarantee the system with finitedistributed delay to be mean-square finite-time bounded.Then,the mean-square finite-time passivity for the system with randomly sensor nonlinearities is proposed.After,the finitedistribution delay is further extended to the infinite-distribution delay,and the corresponding corollaries are obtained.Finally,a numerical example is given to verify the proposed results.In the third part,the H_?filtering problem for uncertain transition probability Markov jump systems with random occurring nonlinearities and missing measurements is investigated.Based on Lyapunov method,the sufficient condition is builded to guarantee the filtering error system to be mean-square stable.Then,a mode-dependent H_?filter is designed.Finally,a numerical example is presented to certify the developed approach.