Model Reduction of Linear Structured Uncertain Systems Using Chebyshev Polynomial Techniques

2005 ◽  
Author(s):  
O. Ismail
Keyword(s):  
Model Reduction ◽  
Chebyshev Polynomial ◽  
Uncertain Systems

Model reduction by Chebyshev polynomial techniques

1979 ◽  
Vol 24 (5) ◽  
pp. 741-747 ◽  
Author(s):  
Y. Bistritz ◽  
G. Langholz
Keyword(s):  
Model Reduction ◽  
Chebyshev Polynomial

scholarly journals Model reduction of multidimensional and uncertain systems

1996 ◽  
Vol 41 (10) ◽  
pp. 1466-1477 ◽  
Author(s):  
C.L. Beck ◽  
J. Doyle ◽  
K. Glover
Keyword(s):  
Model Reduction ◽  
Uncertain Systems

scholarly journals Mixed method of model reduction for uncertain systems

2007 ◽  
Vol 4 (1) ◽  
pp. 1-12 ◽  
Author(s):  
N. Selvaganesan
Keyword(s):  
Steady State ◽  
Model Reduction ◽  
Correction Factor ◽  
Interval Arithmetic ◽  
Mixed Method ◽  
Uncertain Systems ◽  
Uncertain System ◽  
Higher Order ◽  
Reduced Order ◽  
Denominator Polynomial

A mixed method for reducing a higher order uncertain system to a stable reduced order one is proposed. Interval arithmetic is used to construct a generalized Routh table for determining the denominator polynomial of the reduced system. The reduced numerator polynomial is obtained using factor division method and the steady state error is minimized using gain correction factor. The proposed method is illustrated using a numerical example.

scholarly journals Model Reduction of Fuzzy Logic Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Zhandong Yu ◽  
Jinyong Yu ◽  
Hamid Reza Karimi
Keyword(s):  
Model Reduction ◽  
Uncertain Systems ◽  
High Order ◽  
Order System ◽  
Dimensional System ◽  
Error Performance ◽  
Model Approximation ◽  
Fuzzy Logic Systems ◽  
Convex Optimization Problem ◽  
Nonlinear Uncertain Systems

This paper deals with the problem ofℒ2-ℒ∞model reduction for continuous-time nonlinear uncertain systems. The approach of the construction of a reduced-order model is presented for high-order nonlinear uncertain systems described by the T-S fuzzy systems, which not only approximates the original high-order system well with anℒ2-ℒ∞error performance levelγbut also translates it into a linear lower-dimensional system. Then, the model approximation is converted into a convex optimization problem by using a linearization procedure. Finally, a numerical example is presented to show the effectiveness of the proposed method.

Balanced-based model reduction of uncertain systems with interval parameters

2014 ◽  
Vol 22 (12) ◽  
pp. 2958-2969 ◽  
Author(s):  
Yunlong Li ◽  
Xiaojun Wang ◽  
Ren Huang ◽  
Zhiping Qiu
Keyword(s):  
Model Reduction ◽  
Uncertain Systems ◽  
Interval Parameters
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