Master-slave Synchronization Of Typical Nonlinear Systems With Sampled-data Control

Since the mid-1980s,classical nonlinear systems have attracted great attention with their unique advantages.In control area,the neural network system and the Lur'e system have great potential in solving the modeling and control systems with highly nonlinear and uncertain systems.Combining nonlinear systems with sampled-data control provides a new approach to solve system synchronization.In this article,we investigate the master-slave synchronization of typical nonlinear systems with sampled-data through Lyapunov stability theory.The stability criteria of neural network system and Lur'e system with sampled-data control are gained by means of linear matrix inequalities(LMIs)and integral inequality.Then,the appropriate sampled-data control and robust?_?control scheme are given.The article first introduces the research background and significance of nonlinear systems.Then,we investigate a class of time-delay exponential synchronization neural network systems with sampled-data control.A novel piecewise time-dependence Lyapunov-Krasovskii functional(LKF)is constructed to fully capture the available information of the system.Less conservative synchronization criteria are obtained to guarantee the exponential stability of the error system,and then the slave system is synchronized with the master system.In addition,the?_?synchronization of the master-slave Lur'e system with time-varying delays and uncertainties under sampled-data control is studied.By developing some new items,an improved piecewise Lyapunov-Krasovskii function(LKF)is constructed.Some sufficient criteria can be obtained to ensure the stability of the error system and realize the synchronous control of the master-slave system.Finally,the sampled-data controller can be derived by solving a set of linear matrix inequalities(LMIs)with the largest sample interval.Compared with the existing results,numerical simulation examples are used to prove the effectiveness and superiority of the proposed methods.Figure 24;Table 4;Reference 60.