Synchronization Control for T-S Fuzzy Neural Networks With Time Delay: A Novel Event-Triggered Mechanism
Shuqing Gong, Zhenyuan Guo, Shiqin Ou, Shiping Wen, Tingwen Huang
Abstract
A novel aperiodic event-triggered control is adopted to address the synchronization issue of T-S fuzzy neural networks with time delay. This control strategy refers to the execution of control tasks in a control system based on real-time events, rather than following a fixed time interval. It allows for more flexible and faster responses to real-time events, and can reduce the computational load, energy consumption, and system costs. At first, a linear event-triggered control mechanism is formulated, in which its triggering condition includes an exponential term. Subsequently, the synchronization criteria based on linear matrix inequalities (LMIs) are deduced under the formulated event-triggered control. In addition, a novel approach that employs the reduction to absurdity technique is proposed to address the nonexistence of Zeno behavior. Eventually, the proposed theory's efficacy is demonstrated by employing an example and an accompanying simulation.