State‐saturated resilient filtering for nonlinear complex networks under event‐triggering protocol. Issue 2 (5th July 2022)

• Record Type: Journal Article
• Authors: Huang, Cong; Mei, Peng; Ding, Weiping; Shi, Quan; Wu, Qiang
• Is Part Of: Asian journal of control. Volume 25:Issue 2(2023)
• Page Start: 1216
• Page End: 1231
• Publication Date: 2022-07-05
• Subjects: Bernoulli random variables -- event‐triggering mechanism -- nonlinear time‐varying complex networks -- random occurrence of nonlinearities -- resilient filtering -- state saturations; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.2906
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: This article is concerned with the resilient state‐saturated filtering issue for nonlinear complex networks via the event‐triggering protocol. The nonlinear inner coupling is taken into account, thereby better reflecting the nature of the complex networks. A set of Bernoulli‐distributed sequences are introduced to model the randomly occurring nonlinearities with a given probability. The signum function is utilized to characterize the state saturation owing to the physical limits on the system. For the purpose of energy saving, an event‐triggering protocol is adopted to govern the regulation of the transmission. The objective of this article is to develop an event‐triggering resilient filtering for nonlinear complex networks subject to state saturations as well as randomly occurring nonlinearities. By using matrix analysis techniques, we first guarantee the upper bound on the filtering error covariance by means of recursions and subsequently minimize such an upper bound by looking for the proper gain matrix relying on the solutions to two difference equations. Moreover, the performance evaluation of the designed filtering scheme is conducted by analyzing the boundedness of the estimation error in the mean square sense. Finally, an experimental example is exploited to validate the usefulness of the state‐saturated resilient filtering algorithm.