Adaptive Event-triggered Stabilization And State Estimation For Markovian Jump Systems With Time-varying Delays

With the rapid development of network communication technologies,networked control systems have received considered attention because of their own advantages such as low cost,increased system flexibility and easy maintenance.However,the limited network bandwith causes some challenging problems to the networked control system.In this case,an event-triggered control method is proposed to reduce the amount of control task executions while retaining a satisfactory performance of the closed-loop system.In recent years,event-triggered approach has emerged as an alternative approach to minimize the use of the communication resources.However,notice that most of existing ones are based on a static event-triggered scheme.How to design an adaptive event-triggered communication scheme to save the limited communication resources while keeping the desired control performance is still open.This is one of the motivations of this paper.And there are few works in the literature on an adaptive event-triggered control method for Markovian jump system.Above are the motivations of this paper.The main work of this paper is as follows:Firstly,adaptive event-triggered stabilization for Markovian jump systems with time-varying delays is discussed.An adaptive event-triggered scheme is proposed.Based on the Lyapunov functional approach,a controller can be achieved by solving some linear matrix inequalities.Finally,a numerical example is provided to show the effectiveness of the result.Secondly,adaptive event-triggered state estimation for Markovian jump systems with time-varying delays is discussed.An adaptive event-triggered scheme is proposed to tradeoff the relationship between the control performance and the network bandwidth burden.By using Lyapunov stability theory,the sufficient conditions for Markovian system to be exponentially stable in the form of linear matrix inequalities are given.Finally,a numerical simulation is used to verify the validity of the established model and analysis method.