Resilient State Estimation for Complex Dynamic Networks with System Model Perturbation

2020 ◽  
pp. 1-1
Author(s):  
Peihu Duan ◽  
Guannan Lv ◽  
Zhisheng Duan ◽  
Yuezu Lv
Keyword(s):  
State Estimation ◽  
Dynamic Networks ◽  
System Model ◽  
Complex Dynamic

scholarly journals Finite-Time Synchronization of Uncertain Complex Dynamic Networks with Nonlinear Coupling

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yiping Luo ◽  
Yuejie Yao
Keyword(s):  
Finite Time ◽  
Time Synchronization ◽  
Dynamic Networks ◽  
Nonlinear Function ◽  
Time Instant ◽  
Control Input ◽  
Parameter Uncertainties ◽  
Complex Dynamic ◽  
Network Nodes ◽  
Analysis Techniques

The finite-time synchronization control is studied in this paper for a class of nonlinear uncertain complex dynamic networks. The uncertainties in the network are unknown but bounded and satisfy some matching conditions. The coupling relationship between network nodes is described by a nonlinear function satisfying the Lipchitz condition. By introducing a simple Lyapunov function, two main results regarding finite-time synchronization of a class of complex dynamic networks with parameter uncertainties are derived. By employing some analysis techniques like matrix inequalities, suitable controllers can be designed based on the obtained synchronization criteria. Moreover, with the obtained control input, the time instant required for the system to achieve finite-time synchronization can be estimated if a set of LMIs are feasible or an assumption on the eigenvalues of some matrices can be satisfied. Finally, the effectiveness of the proposed results is verified by numerical simulation.

Robust State Estimation for Linear Systems

1993 ◽  
Vol 115 (1) ◽  
pp. 193-196
Author(s):  
S. S. Garimella ◽  
K. Srinivasan
Keyword(s):  
Dynamic System ◽  
State Estimation ◽  
Real Time ◽  
Linear Systems ◽  
Upper Bound ◽  
Estimation Error ◽  
System Model ◽  
Linear Dynamic ◽  
Modeling Errors ◽  
System States

Real-time state estimation of a linear dynamic system using an observer, in the presence of modeling errors in the system model used by the observer and uncertainty in the initial system states, is considered here. A guideline for designing observers for multioutput systems is established, based on an expression for an upper bound on the norm of the state estimation error derived in this paper. An example is presented to illustrate the usefulness of this guideline.

Detecting Influential Spreaders in Complex, Dynamic Networks

Computer ◽  
2013 ◽  
Vol 46 (4) ◽  
pp. 24-29 ◽  
Author(s):  
Pavlos Basaras ◽  
Dimitrios Katsaros ◽  
Leandros Tassiulas
Keyword(s):  
Dynamic Networks ◽  
Complex Dynamic

Detection of local community structures in complex dynamic networks with random walks

2009 ◽  
Vol 3 (4) ◽  
pp. 266-278 ◽  
Author(s):  
G.S. Thakur ◽  
A.W.M. Dress ◽  
R. Tiwari ◽  
S.-S. Chen ◽  
M.T. Thai
Keyword(s):  
Random Walks ◽  
Local Community ◽  
Dynamic Networks ◽  
Community Structures ◽  
Complex Dynamic

scholarly journals Game Theory and Extremal Optimization for Community Detection in Complex Dynamic Networks

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e86891 ◽  
Author(s):  
Rodica Ioana Lung ◽  
Camelia Chira ◽  
Anca Andreica
Keyword(s):  
Game Theory ◽  
Community Detection ◽  
Dynamic Networks ◽  
Complex Dynamic ◽  
Extremal Optimization

Model Formulation of Complex Mechanisms With Multiple Inputs: Part II—The Dynamic Model

1978 ◽  
Vol 100 (4) ◽  
pp. 755-761 ◽  
Author(s):  
C. E. Benedict ◽  
D. Tesar
Keyword(s):  
Dynamic Systems ◽  
Companion Paper ◽  
Qualitative Description ◽  
System Model ◽  
Mechanical Device ◽  
Model Formulation ◽  
Complex Dynamic ◽  
Complex Dynamic Systems ◽  
Influence Coefficients ◽  
External Loads

The system model of a complex multiple degree of freedom mechanical device containing external loads, masses, springs, and dashpots is obtained explicitly in terms of the kinematic influence coefficients given in the companion paper, Part I. The controlling nonlinear coupled differential equations can be obtained by an elementary power balance in terms of this model. The formulation should be effective in developing a qualitative description of complex dynamic systems to treat their dynamic response, vibration, or automatic control during adjustment.

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