A decentralized control strategy for a large-scale system including uncertainties in the dynamic model

Author(s):  
M. Sigut ◽  
L. Acosta ◽  
S. Torres ◽  
J.J. Rodrigo ◽  
A. Hamilton
2015 ◽  
Vol 44 (3) ◽  
pp. 247-253
Author(s):  
Branislav Rehak

A control design for a large-scale system using LMI optimization is proposed. The control is designed in a way such that the LQ cost in the case of the decentralized control  does not exceed a certain limit. The optimized quantity are the values of the control gain matrices. The methodology is useful even for finding a decomposition of the system, however, some expert knowledge is necessary in this case. The capabilities of the algorithm are illustrated by two examples.DOI: http://dx.doi.org/10.5755/j01.itc.44.3.6464


2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Hang Zhang ◽  
Xia Wang ◽  
Yi-Jun Wang ◽  
Zhao-Mei Sun

This paper studies the decentralized stabilization problem for an uncertain fuzzy large-scale system with time delays. The considered large-scale system is composed of several T-S fuzzy subsystems. The decentralized parallel distributed compensation (PDC) fuzzy control for each subsystem is designed to stabilize the whole system. Based on Lyapunov criterion, some sufficient conditions are proposed. Moreover, the positive definite matricesPiand PDC gainKijcan be solved by linear matrix inequality (LMI) toolbox of Matlab. Then, the optimization design method for decentralized control is also considered with respect to a quadratic performance index. Finally, numerical examples are given and compared with those of Zhang et al., 2004 to illustrate the effectiveness and less conservativeness of our method.


2008 ◽  
Author(s):  
Steven M. Bellovin ◽  
Salvatore J. Stolfo ◽  
Angelos D. Keromytis

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