Time-space sampled-data control for semi-Markov reaction-diffusion neural networks: Adopting multiple event-triggered protocols

This study focuses on time-space sampled-data control for semi-Markov reaction-diffusion neural networks (SMRDNNs) utilizing event-triggered protocols (ETPs) and a multiasynchronous strategy. To mitigate data confusion caused by significant transmission delays, a novel packet loss scheduling approach is developed, leading to the formation of a unified SMRDNN model. A hidden semi-Markov model is adopted to address asynchronous dynamics among subsystems, ETPs, and the controller. By simultaneously exploring multiple ETPs in the temporal dimension and sampling mechanisms in the spatial dimension, a new space-time sampled-data control method is devised. This strategy effectively reduces communication resource usage while maintaining control performance. Finally, an illustrative example is provided to demonstrate the effectiveness and superiority of the attained theoretical results.