Robust State Observer Design for Dynamic Connection Relationships in Complex Dynamical Networks
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The complex dynamical network with the time-varying links may be regarded to be composed of the two mutually coupled subsystems, which are called the nodes subsystem and the connection relationships subsystem respectively. Designing the state observer for the nodes subsystem has been discussed in some existing researches by employing the known connection relationships. However, the state observer design for the connection relationships subsystem is not shown in the existing researches. In practical applications, the connection relationships subsystem possesses also the state variables such as the relationship strength in social network, the size of web tension in the web winding system. Therefore, designing the state observer for the connection relationships subsystem is also of practical significance. In this paper, a novel state observer is proposed for the connection relationships subsystem modeled mathematically by the Riccati matrix differential equation. The observer utilizes the output measurements of the connection relationships subsystem and the state of nodes subsystem. Compared with the state observer of nodes subsystems which employs the known connection relationships, the state observer for the connection relationships subsystem employs the state of nodes subsystem and is represented in form of the matrix differential equation. By using Lyapunov stability theory, it is proved in this paper that the state observer for the connection relationships subsystem is asymptotical under certain mathematic conditions. Finally, the illustrative simulation is given to show the efficiency and validity of the proposed method in this paper.
KeywordsComplex dynamical network connection relationships subsystem Riccati matrix differential equation state observer
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- A. Villani, A. Frigessi, F. Liljeros, M. K. Nordvik, and B. F. Blasio, “A characterization of Internet dating network structures among nordic men who have sex with men,” PLoS One, vol. 7. no. 7, pp. e39717, July 2012.Google Scholar
- M. E. J. Newman, “The structure and function of complex networks,” Siam Review, vol.45, no.2, pp. 167–256, June 2003.Google Scholar
- H. J. Savino, C. R. P. D. Santos, F. O. Souza, L. C. A. Pimenta, M. D. Oliveira, and R. M. Palhares, “Conditions for consensus of multi–ggent systems with time–delays and uncertain switching topology,” IEEE Transactions on Industrial Electronics, vol. 63, no. 2, pp. 1258–1267, November 2015.CrossRefGoogle Scholar
- X. Wu, G. P. Jiang, and X. W. Wang, “State estimation for general complex dynamical networks with packet loss,” IEEE Transactions on Circuits & Systems II: Express Briefs, pp. 99, October 2017.Google Scholar
- L. Zou, Z. D. Wang, H. J. Gao, and X. H. Liu, “State estimation for discrete–time dynamical networks with time–varying delays and stochastic disturbances under the round–robin protocol,” IEEE Transactions on Neural Networks & Learning Systems, vol. 18, no. 1, pp. 194–208, January 2013.Google Scholar
- H. J. Li, Z. J. Ning, Y. H. Yin, and Y. Tang, “Synchronization and state estimation for singular complex dynamical networks with time–varying delays,” Communications in Nonlinear Science & Numerical Simulation, vol. 3, no. 2, pp. 213–225, June 1991.Google Scholar
- J. Hu, Z. D. Wang, S. Liu, and H. J. Gao, “A varianceconstrained approach to recursive state estimation for timevarying complex networks with missing measurements,” Automatica, vol. 64, no. C, pp. 155–162, February 2016.Google Scholar
- Z. L. Gao and Y. H. Wang, “The structural balance analysis of complex dynamical networks based on nodes’ dynamical couplings,” PLoS One, vol. 13. no. 1, pp. e0191941, January 2018.Google Scholar
- T. V. Antonio, V. D. Paul, and D. L. Patrick, “Dynamical models explaining social balance and evolution of cooperation,” Plos One, vol. 8. no. 4, pp. e60063, April 2013.Google Scholar