TY  - JOUR
AU  - Niu, Yuchen
AU  - Shi, Kaibo
AU  - Cai, Xiao
AU  - Wen, Shiping
PY  - 2025
TI  - Adaptive smooth sampled-data control for synchronization of T–S fuzzy reaction-diffusion neural networks with actuator saturation
JO  - AIMS Mathematics
SP  - 1142
EP  - 1161
VL  - 10
IS  - 1
AB  - 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.
UR  - https://doi.org/10.3934/math.2025054
DO  - 10.3934/math.2025054