A Dynamic Event-Triggered Approach to Recursive Filtering for Complex Networks With Switching Topologies Subject to Random Sensor Failures

• Published in: IEEE transaction on neural networks and learning systems Vol. 31; no. 10; pp. 4381 - 4388
• Main Authors: Li, Qi, Wang, Zidong, Li, Nan, Sheng, Weiguo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Complex networks; Complex networks (CNs); Covariance; dynamic event-triggered mechanisms (ETMs); Dynamic scheduling; Failure; Filters; Heuristic algorithms; IIR filters; Markov chains; Markov processes; Network topologies; recursive filtering; Recursive methods; sensor failures; Sensors; Stochasticity; Switches; Switching; switching topologies; Topology; Upper bounds
• DOI: 10.1109/TNNLS.2019.2951948
• ISSN: 2162-237X; 2162-2388

This article deals with the recursive filtering issue for a class of nonlinear complex networks (CNs) with switching topologies, random sensor failures and dynamic event-triggered mechanisms. A Markov chain is utilized to characterize the switching behavior of the network topology. The phenomenon of sensor failures occurs in a random way governed by a set of stochastic variables obeying certain probability distributions. In order to save communication cost, a dynamic event-triggered transmission protocol is introduced into the transmission channel from the sensors to the recursive filters. The objective of the addressed problem is to design a set of dynamic event-triggered filters for the underlying CN with a certain guaranteed upper bound (on the filtering error covariance) that is then locally minimized. By employing the induction method, an upper bound is first obtained on the filtering error covariance and subsequently minimized by properly designing the filter parameters. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed filtering scheme.