Event-triggered L2–L∞ state estimation for discrete-time neural networks with sensor saturations and data quantization. Issue 17 (November 2019)

• Record Type: Journal Article
• Authors: Wang, Huijiao; Dong, Ruihua; Xue, Anke; Peng, Yan
• Is Part Of: Journal of the Franklin Institute. Volume 356:Issue 17(2019)
• Page Start: 10216
• Page End: 10240
• Publication Date: 2019-11
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2018.01.038
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: The problem of event-triggered L 2 – L ∞ state estimation for a class of discrete-time neural networks with mixed time delays, sensor saturations and data quantization is investigated in this paper. The mixed time delays include discrete and distributed delays. Due to the constraint of physical devices, the measurement outputs are partially nonlinear and subjected to sensor saturations. In order to save the limited communication resource, an adaptive event-triggered mechanism is employed to determine whether or not the current sampled data should be transmitted. Data quantization is also an effective way to decrease the amount of transmitted data via networks. Some delay-dependent sufficient conditions have been derived to guarantee the augmented estimator system is asymptotic stable and achieve the prescribed L 2 – L ∞ performance. The design process of L 2 – L ∞ estimator is also derived. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.