Network-based passive estimation for switched complex dynamical networks under persistent dwell-time with limited signals

• Published in: Journal of the Franklin Institute Vol. 357; no. 15; pp. 10921 - 10936
• Main Authors: Wang, Yudong, Hu, Xiaohui, Shi, Kaibo, Song, Xiaona, Shen, Hao
• Format: Journal Article
• Language: English
• Published: Elmsford Elsevier Ltd 01.10.2020; Elsevier Science Ltd
• Subjects: Asymptotic methods; Dynamic stability; Estimating techniques; Linear matrix inequalities; Measurement; Networks; Neural networks; State estimation; Studies; Switching; Systems stability
• ISSN: 0016-0032; 1879-2693; 0016-0032
• DOI: 10.1016/j.jfranklin.2020.08.037

In this paper, the state estimation issue for a set of switched complex dynamic networks affected by quantization is studied, in which the switching process is assumed to follow persistent dwell-time switching regulation. Thereinto, the switching regulation aforementioned describes the switchings among different parameters on complex dynamic networks. Meanwhile, for the network-based model, in the communication channels from the sensor to the estimator, quantization is inevitable to be taken into consideration. To track partially inaccessible information in the target system, a state estimator is thoroughly reconstructed. Intensive attention is that a set of sufficient conditions can be derived by using some simple matrix transformation methods, linear matrix inequality and Lyapunov stability theory, to further assure the error dynamic obtained is globally uniformly exponentially stable and meets passive property. The serviceability of the state estimator gains solved is finally verified and the effectiveness of the proposed design approach is further illustrated.