Adaptive smooth sampled-data control for synchronization of T–S fuzzy reaction-diffusion neural networks with actuator saturation
Yuchen Niu, Kaibo Shi, Xiao Feng Cai, Shiping Wen · AIMS Mathematics · 2025
This paper addresses the synchronization issue in T–S fuzzy reaction–diffusion neural networks (TFRNNs) with time-varying delays and actuator saturation. First, an adaptive smooth sampled-data (ASSD) controller is proposed to optimize communication resources. In the ASSD controller, the dynamic forgetting factor is employed to process historical data smoothly, thereby preventing data distortion due to unexpected events. Second, the Lyapunov–Krasovskii functional (LKF), which captures more information about the system, is introduced, and it can provide greater flexibility than the fixed-matrix LKF. Meanwhile, by employing the semi-looped-functional method, the constraint for negative determination of the sum of its derivatives is removed, which enhances the feasibility of expanding the solution. Consequently, a novel criterion and the corresponding algorithm are established to obtain the larger maximum allowable sampling interval (MASI). Finally, simulations demonstrate the effectiveness and superiority of the proposed theoretical results.