Adaptive event-triggered distributed recursive filtering with stochastic parameters and faults. (January 2022)

• Record Type: Journal Article
• Authors: Wu, Lingling; Ding, Derui; Ju, Yamei; Yi, Xiaojian
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 44:Number 2(2022)
• Page Start: 424
• Page End: 434
• Publication Date: 2022-01
• Subjects: Adaptive event-triggering protocol -- stochastic parameters -- randomly occurring faults -- distributed filtering -- joint estimation; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/01423312211037965
• Languages: English
• ISSNs: 0142-3312

Abstract

This paper investigates the distributed recursive filtering issue of a class of stochastic parameter systems with randomly occurring faults. An event-triggered scheme with an adaptive threshold is designed to better reduce the communication load by considering dynamic changes of measurement sequences. In the framework of Kalman filtering, a distributed filter is constructed to simultaneously estimate both system states and faults. Then, the upper bound of filtering error covariance is derived with the help of stochastic analysis combined with basis matrix inequalities. The obtained condition with a recursive feature is dependent on the statistical characteristic of stochastic parameter matrices as well as the time-varying threshold. Furthermore, the desired filter gain is derived by minimizing the trace of the obtained upper bound. Finally, two simulation examples are conducted to demonstrate the effectiveness and feasibility of the proposed filtering method.