Network-based robust filtering for Markovian jump systems with incomplete transition probabilities

In this paper, the H∞ filtering problem is investigated for a class of discrete-time Markovian jump nonlinear systems with partly unknown transition probabilities and subject to sensor saturation over unreliable communication. The description of researched plant includes global Lipschitz nonlinearities and state-dependent random noise and external-disturbance. A decomposition approach is used to deal with the characteristic of sensor saturation. Since the communication links between the plant and filter lack enough reliability, the effects of output quantization and data packet losses should both be considered. The proposed quantizer's parameter is on-line updating and the corresponding practical adjusting rule can ensure the dynamic performance of the controlled system. Among different operation modes, the cross coupling between system matrices and Lyapunov matrices is disposed by introducing proper slack matrix variables. The purpose of this work is to design a full-order filter based on incomplete output measurements in order to guarantee the stochastic stability of the estimation error. Precise expression of the filters and related analysis are depicted in this paper. Finally, a numerical simulation is provided to show the effectiveness of the designing filtering method.