Finite-time P2P filtering for hidden Markov jump systems with adaptive memory event-triggered mechanism: A packet loss compensation strategy
Nan Wang, Lu Yu · Applied Mathematics and Computation · 2026
In this paper, the problem of finite-time packet loss compensation peak-to-peak (P2P) filtering for a class of Takagi-Sugeno fuzzy hidden Markov jump systems based on the mode-dependent adaptive memory event-triggered mechanism (AMETM) is studied. A hidden Markov model is used to describe the asynchronous phenomenon between the system and the filter. At the same time, assuming that the packet is discarded in the form of mode-dependent transition probability uncertainty before transmission to the filter, an event-triggered single exponential smoothing packet loss compensation scheme is designed to reduce the impact of network congestion and packet loss. In addition, to balance the relationship between P2P performance and data transmission, a mode-dependent AMETM with two dynamic threshold functions is improved. Furthermore, the sufficient conditions for the designed filter are obtained by using the fuzzy-dependent and mode-dependent Lyapunov functions to ensure the stochastic finite-time boundedness and satisfy the predetermined P2P performance. Finally, the effectiveness and superiority of the proposed method are verified by an example of a single-link manipulator system.