$\mathcal {H}_{\infty }$ State Estimation for PDT-Switched Coupled Neural Networks Under Round-Robin Protocol: A Cooperation-Competition-Based Mechanism

This paper is concentrated on the $\mathcal {H_{\infty }}$ state estimation problem for switched coupled neural networks based on a Takagi-Sugeno fuzzy model. Notably, the time-variant network topology with alternate fast switchings and slow ones is described suitably by a persistent dwell-time rule, and the interactive dynamics with both cooperative properties and antagonistic ones among nodes are featured comprehensively by the switching signed graph. In view of the communication pressures brought by network-induced problems and the requirements in digital control, the round-robin protocol and logarithmic quantization are flexibly integrated for more transmission efficiency and fewer data collisions. Thereafter, by utilizing a relaxed multiple Lyapunov function method and some novel matrix process techniques, sufficient criteria guaranteeing the exponential stability in a globally uniform sense with a prescribed $\mathcal {H_{\infty }}$ performance level of the estimation error system are established. Finally, the synthesized analysis of the proposed method is presented with an illustrative example.