Fuzzy-Model-Based H∞ Reduced-Order Filtering for Semi-Markov Jump Nonlinear Networked Systems Under Weighted Try-Once-Discard Protocol

The ${\mathcal {H}}_{\infty }$ reduced-order filtering problem for nonlinear semi-Markov jump systems is investigated in this work. To tackle the nonlinearity of the plant, the Takagi–Sugeno fuzzy approach is adopted. Considering the limited network resources, the logarithmic quantizer and the weighted try-once-discard protocol are used to relieve the communication load for networks and avoid data congestion. Then, the reduced-order filter is constructed by combining the processed data and the designed slack matrix. On the basis of the fuzzy-model-based method and the Lyapunov theory, some criteria are established to ensure that the filtering error system is mean-square exponentially stable. Finally, the superiority and validity of the results obtained in this work are proved by two illustrative examples.