Fuzzy-Model-Based Asynchronous Fault Detection for Markov Jump Systems With Partially Unknown Transition Probabilities: An Adaptive Event-Triggered Approach

This article addresses the event-triggered asynchronous fault detection (FD) problem of fuzzy-model-based nonlinear Markov jump systems (MJSs) with partially unknown transition probabilities. For this objective, plant is modeled as an interval type-2 (IT2) fuzzy MJS aid IT2 sets capturing uncertainties membership functions. An adaptive scheme introduced to bring down costs communication network from system filter (FDF), in which triggering parameter can be adaptively tuned dynamics. A hidden model (HMM) employed characterize phenomenon between and FDF. Unlike existing results, probabilities FDF are allowed known. By using Lyapunov membership-function-dependent methods, existence conditions derived. Finally, proposed FD methods verified by a numerical simulation.