Decentralized H/sub ∞/ controller design for large-scale systems: a matrix inequality approach using a homotopy method

2002 ◽  
Author(s):  
M. Ikeda ◽  
G. Zhai ◽  
Y. Fujisaki
Keyword(s):  
Large Scale ◽  
Controller Design ◽  
Homotopy Method ◽  
Matrix Inequality ◽  
Large Scale Systems

Robust Nonfragile Decentralized Controller Design for Uncertain Large-Scale Interconnected Systems With Time-Delays

2002 ◽  
Vol 124 (2) ◽  
pp. 332-336 ◽  
Author(s):  
Ju H. Park
Keyword(s):  
Time Delays ◽  
Large Scale ◽  
Lyapunov Method ◽  
Controller Design ◽  
Linear Matrix ◽  
Sufficient Condition ◽  
Matrix Inequality ◽  
Large Scale Systems ◽  
Controller Gain ◽  
Decentralized Controllers

This paper describes the synthesis of robust nonfragile decentralized controllers for uncertain large-scale systems with time-delays in the subsystem interconnections and controller gain variations. Based on the Lyapunov method, a sufficient condition for robust stability is derived in terms of a linear matrix inequality (LMI), and the measure of nonfragility in controller is presented.

Non-Fragile Decentralized Control for the Uncertain Singular Large-Scale Systems with Time-Delay

2011 ◽  
Vol 317-319 ◽  
pp. 2204-2207
Author(s):  
Dong Mei Yang ◽  
Qing Sun
Keyword(s):  
Time Delay ◽  
Linear Matrix Inequalities ◽  
Decentralized Control ◽  
Large Scale ◽  
Controller Design ◽  
Linear Matrix ◽  
Sufficient Condition ◽  
Matrix Inequalities ◽  
Large Scale Systems ◽  
System With Time Delay

This paper is concerned with the non-fragile decentralized controller design problem for uncertain singular large-scale system with time-delay. Sufficient condition for the controller is expressed in terms of linear matrix inequalities(LMIs). When this condition is feasible, the desired controller can be obtained with additive gain perturbations and multiplicative gain perturbations. Finally, a numerical example is also given to illustrate the effectiveness.

scholarly journals Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Huanqing Wang ◽  
Qi Zhou ◽  
Xuebo Yang ◽  
Hamid Reza Karimi
Keyword(s):  
Neural Control ◽  
Large Scale ◽  
Controller Design ◽  
Dead Zone ◽  
Design Procedure ◽  
Rbf Neural Networks ◽  
Large Scale Systems ◽  
Dead Zones ◽  
Stabilization Control ◽  
Control Scheme

The problem of robust decentralized adaptive neural stabilization control is investigated for a class of nonaffine nonlinear interconnected large-scale systems with unknown dead zones. In the controller design procedure, radical basis function (RBF) neural networks are applied to approximate packaged unknown nonlinearities and then an adaptive neural decentralized controller is systematically derived without requiring any information on the boundedness of dead zone parameters (slopes and break points). It is proven that the developed control scheme can ensure that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded in the sense of mean square. Simulation study is provided to further demonstrate the effectiveness of the developed control scheme.

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