The Sampling Of The Sampling System Depends On The Stability Study

Sampling is a common phenomenon in computer,communication and network systems,and stability is the premise of the control system.The stability of sampled-data control systems has important theoretical and practical value.This paper studies the stability of sampled-data control systems and reveals the relationship between sampling interval and stability.Firstly,the stability of linear sampled-data systems is studied by using the Lyapunov-like functional method,and less conservative stability results are obtained.The stability results are further extended to the uncertainty of convex polygon parameters,and the sampling dependent robust stability criterion is obtained.Secondly,the synchronization analysis is transformed into asymptotic stability analysis,and the synchronization conditions are obtained by using Lyapunov-like functional method.Finally,the effectiveness and superiority of the condition are verified by simulation.Chapter 1 briefly introduces the research background and research status of the sampled-data system,and points out the main problems to be studied in this paper.Chapter 2 introduces the basic theoretical knowledge used,as well as the related lemma needed,to provide the main theoretical basis for the following research.Chapter 3 studies the sampling dependence stability of linear sampled-data control systems by constructing the new Lyapunov-like functional.This kind of the Lyapunov-like functional improves the previous Lyapunov functional,introduces the double integral of the Lyapunov function,which makes full use of the information of the sampling system in the sampling interval(t,t_k+1).In order to estimate the derivative of this kind of the Lyapunov-like functional,introduce an integral inequality which is higher than Jenson inequality,and obtain a tighter upper bound.The result of conservative stability is smaller.Then,the stability result is further extended to the case with convex polygon parameter uncertainty,and the robust stability criterion is derived.Chapter 4 the synchronization of sampling saturation control systems in nonlinear neural network is studied.The error system is derived from the master system system and the slave system,then the synchronization of the master system and the slave system is transformed into the stability of the error system.A new Lyapunov-like function is constructed for the error system.The derivative of the Lyapunov-like function is estimated by using the new inequality,and the stability result is derived.Thus,the synchronization condition of the nonlinear neural network sampling control system is obtained.The simulation results show that the synchronization conditions are effective and conservative.Chapter 5 briefly summarizes the research content of this paper,and points out the prospect of the future study work.