Aperiodic Sampled-Data-Based Control for T-S Fuzzy Systems: An Improved Fuzzy-Dependent Adaptive Event-Triggered Mechanism

Abstract

This paper is devoted to designing a novel aperiodic sampled-data-based event-triggered control strategy for Takagi-Sugeno (T-S) fuzzy systems. Firstly, via taking the structural features of fuzzy subsystems and the available information of fuzzy membership functions (FMFs) into consideration, an improved fuzzy-dependent adaptive event-triggered mechanism (FDAETM), which designs different adaptive event-triggered mechanisms for corresponding fuzzy subsystems, is proposed to provide extra design flexibility and further optimize communication efficiency. Then, the two-side looped-functional method (TSLFM) and dynamic partitioning approach are introduced in the construction of the novel Lyapunov-Krasovskii functional (LKF). These two methods contribute to deriving preferable stability criterion and stabilization approach via relaxing the positive definite constraint on LKF and fully utilizing the inner system state during the whole aperiodic sampling interval. Eventually, two simulation examples are introduced to verify the effectiveness of the proposed control strategy and its advantages in lightening communication frequency.