Robust Event-Triggered Control for Networked Fuzzy Systems

Abstract

This paper presents colorbluea Neural Gradient Descent-based Event-Triggered (NGDET) control design colorbluefor Networked Control Systems (NCS) based on Takagi-Sugeno (T-S) fuzzy systems affected by measurement noise, external disturbances, and network-induced delays. The main objective is to improve communication efficiency while preserving closed-loop stability and robust performance. To this end, an adaptive event-triggering mechanism is proposed, where the triggering threshold is updated online through a colorblueneural gradient descent technique according to the system behavior and communication network constraints. Then, colorblue by constructing an appropriate Lyapunov–Krasovskii Functional (LKF), colorblue relaxed Linear Matrix Inequality (LMI) conditions are developed for the design of a networked controller involving a sampled data Parallel Distributed Compensation (PDC) approach ensuring asymptotic stability colorblue and robustness of NCS closed-loop dynamic. The simulation results demonstrate its effectiveness by enlarging the admissible delay bound, reducing packet transmissions, and achieving less conservative performance compared to existing methods from the literature, while preserving robustness against disturbances and measurement noise. newline noindent Keywords: Networked control systems, Takagi–Sugeno fuzzy systems, Event-triggered control, colorblue Neural gradient descent, measurement noise.