Control And Filtering For Several Classes Of Nonlinear Markov Jump Systems

Due to the precision,reliability,and flexibility of mathematical modeling and theoretical analysis of the actual system under study,Markov jump nonlinear system has attracted much attention of many scholars at home and abroad.Based on the existing research results,this dissertation focuses on the control and filtering problems of Markov jump nonlinear systems.The main contents are focused on the hybrid filter design,finitetime filter design,finite-time asynchronous controller design,event-triggered controller design,etc.The details are given as follows:1.The dissipative filtering problem for a class of stochastic jumping neural networks is concerned.The model under consideration is subject to unreliable communication links,which result in some network-induced phenomena.A set of Bernoulli distributed white sequences are introduced to govern these phenomena occurring in a random way.Based on the proposed scenario,a mixed filter is designed,which ensures that the filtering error system is extended stochastically dissipative.With the help of Lyapunov-Krasovskii methodology and an improved matrix decoupling approach,sufficient conditions for the existence of such a filter are presented.And the corresponding theory is verified by experimental simulation.2.The resilient H_? filter design problem for discrete-time uncertain Markov jump neural networks with packet dropouts is investigated.The purpose is to design a filter which not only subjects to an H_? performance level but also is insensitive with respect to filter gain uncertainties.The data packet dropouts phenomenon modeled by a stochastic Bernoulli distributed process is also considered.Some sufficient conditions in the form of linear matrix inequalities are established which guarantee that the filtering error system is finite-time bounded.Finally,the corresponding design method was verified by experimental simulation.3.The problem of finite-time H_? control for Markov jump repeated scalar nonlinear systems(MJRSNSs)with input constraints is concerned.With the aid of LyapunovKrasovskii methodology,some H_? performance criteria for the MJRSNSs are proposed.Based on these criteria,sufficient conditions are established to ensure that the designed controller meets the desired control requirements.Finally,the experimental simulation is used to verify the corresponding theory.4.The extend passive control problem for networked nonlinear semi-Markov jump systems based on an event-triggered mechanism(ETM)is addressed.For fear of the wasting of communication resources,an ETM to screen the transmitted data is adopted for the T-S fuzzy systems under consideration.Owing to the use of some novel integral inequalities based on auxiliary functions,some sufficient conditions are obtained to ensure that the resulting closed-loop system is stochastically stable with an extend passive performance.Lastly,the availability of the proposed design method is extensively described and validated via its application to a truck model.