Synchronization Control For T-S Fuzzy Neural Networked Systems With Limited Communication

Neural networks are a kind of nonlinear dynamic systems which can exhibit a great of complex dynamic behaviors when the parameters are properly selected.T-S fuzzy system is a nonlinear system described by a set of IF-THEN rules.It can convert the entire input space into multiple local fuzzy spaces,and each output space is represented by a system of linear equations.Many linear system theories can be easily applied to the system analysis of nonlinear dynamic systems by giving a local linear representation of the nonlinear system.In addition,synchronous control also has considerable potential in practical applications,and related control issues have attracted wide attention.On the other hand,due to the limitations of network resources and some hardware facilities,the problem of limited communication is also an important topic in the field of networked systems.Therefore,the research on synchronization control of T-S fuzzy neural network systems with limited communication is of great significance.Based on the neural network system,this paper systematically investigates the synchronization control problem of T-S fuzzy neural network system with restricted communication.The main research contents are as follows:(1)Based on considerations of network attacks and input saturation,the adaptive event-triggered drive-response synchronization control problem for neural networked systems is studied.Firstly,considering the effects of network attack and control input saturation,a closed-loop error system model based on drive-response synchronization is constructed.By using the Lyapunov-krasovskii functional method,sufficient conditions are obtained to guarantee the asymptotic stability of the error systems.On this basis,by using the linear matrix inequalities(LMIs)method,the gain matrix of the output feedback controller and event trigger parameter are obtained.Finally,a numerical example is given to illustrate the effectiveness of the method.(2)An event-triggered synchronization control problem for a class of T-S fuzzy neural network systems with time delay is studied.An event-triggered scheme based on output error is proposed to update the control input signal to improve the communication efficiency.Firstly,a T-S model of synchronization control based on the drive-response mode is proposed to study the synchronization problem under the given event-triggered scheme.Then,by using the Lyapunov functional method,sufficient conditions for asymptotic stability of synchronization error systems are obtained.Moreover,by solving some LMIs,a set of controllers for the response systems can be achieved.Finally,a numerical example is provided to illustrate effectiveness of the proposed method.(3)Considering the impact of network attacks in channel transmission,theadaptive event-triggered non-fragile synchronization secure control problem of the T-S fuzzy neural network system is studied.An adaptive event-triggered scheme is proposed,which can dynamically adjust the triggering threshold according to the output error.Firstly,considering the effects of adaptive event-triggering scheme and cyber-attacks on signal transmission,a synchronization error system model is established.Then,sufficient conditions for the asymptotic stability of the error system are obtained by employing Lyapunov functional method.Moreover,by using LMIs method,the design problem of non-fragile T-S fuzzy controller is solved.Finally,a numerical simulation is given to illustrate the effectiveness of the proposed method.