Asynchronous Filtering for Fuzzy Sampled-Data Systems Subject to Censored Measurements and Nonuniform Sampling
Jun Cheng, Qiongwen Zhang, Dan Zhang, Huaicheng Yan, Wenhai Qi
Abstract
This article focuses on addressing the asynchronous filtering problem in T-S fuzzy sampled-data systems under the presence of censored measurements and non-uniform sampling. In contrast to existing approaches that rely on Markov processes to govern nonuniform sampling, a sojourn probability strategy is adopted to capture the random behavior of sampling periods. This strategy provides easily measurable information, surpassing the complexities associated with obtaining transition probabilities. Moreover, the variations in nonhomogeneous sojourn probability are controlled by a deterministic switching signal, enabling wider applicability compared to existing methods. The fading degree of transmitted data are assessed through a censoring strategy, and the measurement is modeled using a Tobit model. Considering the presence of nonuniform sampling periods and asynchronous premise variables, a general asynchronous fuzzy filtering method is carried out, and sufficient conditions are derived to ensure the mean-square stability of the resulting system. Finally, the developed methodology is validated through a numerical example, showcasing its applicability and effectiveness.