Protocol-Based Sampled-Data Control for T-S Fuzzy Reaction–Diffusion Neural Networks

This study explores the protocol-based sampled-data control for T-S fuzzy reaction–diffusion neural networks (RDNNs) with nonhomogeneous sojourn probabilities (NSPs). By incorporating a deterministic switching signal, a new framework of NSPs is developed to characterize the random behaviors of fuzzy RDNNs. Unlike previous studies, this work introduces multiasynchronous switching among fuzzy RDNNs, triggering conditions, and controllers using dynamic asynchrony models, effectively capturing mode transitions through NSPs and detection probabilities. An improved adaptive event-triggered protocol is created by integrating fuzzy rules and detection probability information. Moving beyond traditional time-domain sampled-data control strategies, a space–time sampled-data control approach is proposed to significantly reduce communication load. Benefiting from Lyapunov theory, criteria are attained for ensuring the mean-square exponential stability of the systems under consideration. Ultimately, the proposed space–time sampled-data control strategy is validated through a simulation example, highlighting its effectiveness and superiority.