Finite-horizon H∞ state estimation for time-varying complex networks based on the outputs of partial nodes

• Main Authors: Wenhua Zhang, Li Sheng, Ming Gao
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-05-01
• Series: Systems Science & Control Engineering
• Subjects: time-varying complex network; resilient filter; partial-nodes-based estimation; $ h_\infty $ state estimation
• Online Access: http://dx.doi.org/10.1080/21642583.2020.1837691

In this paper, the partial-nodes-based resilient filtering problem for a class of discrete time-varying complex networks is investigated. In order to reduce the effect of imprecision of filter parameters on estimation performance, a set of resilient filters is proposed. The measurement output from all network nodes may not be available in the actual system, but only from a fraction of nodes. The state estimators are designed for the time-varying complex network based on partial nodes to make the estimation error achieve the $ H_\infty $ performance constraint over a finite horizon. By employing the completing-the-square technique and the backward recursive Riccati difference equations, the sufficient conditions for the existence of the estimator are derived. Then the gain of the estimator is calculated. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.