Distributed filtering for nonlinear time‐delay systems over sensor networks subject to multiplicative link noises and switching topology

• Published in: International journal of robust and nonlinear control Vol. 29; no. 10; pp. 2941 - 2959
• Main Authors: Ma, Lifeng, Wang, Zidong, Liu, Yurong, Alsaadi, Fuad E.
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 10.07.2019
• Subjects: Attenuation; average dwell time; Computer simulation; Convexity; distributed filtering; Dwell time; Error detection; Filtration; multiplicative link noises; Nodes; Nonlinear systems; Optimization; Sensors; Switching theory; switching topology; Time delay systems; Topology
• ISSN: 1049-8923; 1099-1239
• DOI: 10.1002/rnc.4535

Summary This paper is concerned with the distributed filtering problem for a class of nonlinear time‐delay system over sensor networks subject to multiplicative link noises and switching topology. Both discrete and distributed time delays are included in the system model. Each sensor estimates the system state by means of the measurements not only from itself but also from its neighboring nodes according to an interactive topology. The multiplicative stochastic link noises are taken into consideration to reflect the random perturbations during the information exchanges between sensor nodes. The considered communication topology is switching according to certain predetermined rules. The purpose of the addressed problem is to develop a distributed filtering strategy such that, in the presence of multiplicative stochastic link noises and switching topology, the resulting filtering error dynamics is exponentially stable in the mean square sense and also satisfies the prespecified weighted disturbance attenuation level. In light of the average dwell time technique in combination with stochastic analysis, sufficient conditions are derived for the solvability of the addressed distributed filtering problem, and the desired filtering gains are then obtained through solving certain convex optimization problems. An illustrative simulation example is presented to demonstrate the correctness and applicability of the obtained theoretical results.