HMM-based asynchronous H∞ filtering for interval type-2 fuzzy Markov jump systems with dynamic quantization and uncertain transition probabilities

Based on the interval type-2 (IT2) fuzzy approach, this paper addresses the asynchronous $H_{\infty }$ filtering for nonlinear Markov jump systems with dynamic quantization and uncertain transition probabilities. The hidden Markov model (HMM) solves the asynchronous issue between the filter and system modes. Meanwhile, the system has uncertain transition probabilities, which means the transition probability matrix and conditional probability matrix investigated in this paper are partially unknown. Furthermore, the IT2 fuzzy approach, with upper and lower membership functions, has been devoted to processing parameter uncertainty in nonlinear systems. The measurement output is subjected to a quantization process facilitated by a dynamic quantizer before its transmission. Ultimately, the stochastic stability criterion of the desired asynchronous $H_{\infty }$ filtering for IT2 fuzzy systems is effectively guaranteed, and an example illustrates the efficacy of the desired filter design methodology.