Event-triggered real-time scheduling stabilization of discrete-time Takagi-Sugeno fuzzy systems via a new weighted matrix approach

The problem of event-triggered real-time scheduling stabilization of Takagi-Sugeno fuzzy system is investigated in this paper. Firstly, a weighted matrix approach is introduced in order to exploit implicit information of a class of nonlinear plants more freely. Secondly, by taking account of the joint-distribution of both past and current normalized fuzzy weighting functions, an event-triggered real-time scheduler is proposed to decide which control mode is to be activated at each sampling instant. Specifically, all the activated control modes can be duly updated with their respective gained matrices for accommodating different time-variant situations provided that the current joint-distribution varies. As a result, the existing stabilization implementation quality can be significantly improved while the computational burden does not increase. Finally, the effectiveness and merits of the proposed method are verified via two simulation examples.