Distributed resilient state estimation for nonlinear systems with randomly occurring communication delays and missing measurements. (2nd June 2021)

• Record Type: Journal Article
• Authors: Qian, Wei; Guo, Simeng; Zhao, Yunji; Xu, Xiaozhuo; Fei, Shumin
• Other Names: Baldi Simone guestEditor.; Kosmatopoulos Elias guestEditor.; Roy Spandan guestEditor.; Yuan Shuai guestEditor.; Roy Sayan Basu guestEditor.; Li Le guestEditor.
• Is Part Of: International journal of adaptive control and signal processing. Volume 36:Number 2(2022)
• Page Start: 315
• Page End: 333
• Publication Date: 2021-06-02
• Subjects: distributed resilient state estimation -- Lyapunov–Krasovskii functional (LKF) -- missing measurements -- random delays -- sensor network; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3281
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: This article is concerned with the distributed H ∞ resilient state estimation problem for a class of nonlinear systems with randomly occurring communication delays and missing measurements in sensor networks. A novel sensor model is proposed, in which two Bernoulli distributed white sequences are introduced to describe the random communication delay and missing measurements in a unified framework. Meanwhile, the estimator gain is allowed to fluctuate within a certain range. Based on the developed model, a novel Lyapunov–Krasovskii functional with multiple delay information terms is constructed, then the stochastic analysis technique and the extended integral inequality are used to calculate the functional derivative. Consequently, the existence conditions for the required distributed estimator are established to ensure that the estimation error system is asymptotically mean‐square stable and satisfies the prescribed H ∞ performance constraint, and the desired gain of distributed resilient estimator is also solved by linearizing the nonlinear terms. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm.