A reduced-order framework applied to linear systems with constrained controls

1996 ◽  
Vol 41 (2) ◽  
pp. 249-255 ◽  
Author(s):  
E.B. Castelan ◽  
J.M. Gomes da Silva ◽  
J.E.R. Cury
Keyword(s):  
Linear Systems ◽  
Reduced Order ◽  
Constrained Controls

Reduced order positive filter design for positive uncertain discrete-time linear systems*

2021 ◽  
pp. 1-1
Author(s):  
Amanda Spagolla ◽  
Cecilia F. Morais ◽  
Ricardo C. L. F. Oliveira ◽  
Pedro L. D. Peres
Keyword(s):  
Linear Systems ◽  
Discrete Time ◽  
Filter Design ◽  
Reduced Order ◽  
Time Linear

Robust reduced-order l2−l∞ filtering for network-based discrete-time linear systems

2015 ◽  
Vol 109 ◽  
pp. 110-118 ◽  
Author(s):  
Zhuo Zhang ◽  
Zexu Zhang ◽  
Shichun Yang
Keyword(s):  
Linear Systems ◽  
Discrete Time ◽  
Reduced Order ◽  
Time Linear

scholarly journals Study of methods for dimension reduction of complex dynamic linear systems models

2018 ◽  
Vol 226 ◽  
pp. 04036
Author(s):  
Yuriy M. Manatskov ◽  
Torsten Bertram ◽  
Danil V. Shaykhutdinov ◽  
Nikolay I. Gorbatenko
Keyword(s):  
Linear Systems ◽  
Main Idea ◽  
Computation Time ◽  
Order Reduction ◽  
Complex Dynamic ◽  
Model Order ◽  
Reduced Order ◽  
Advantages And Disadvantages ◽  
Optimization Stage ◽  
Linear Systems Of Equations

Complex dynamic linear systems of equations are solved by numerical iterative methods, which need much computation and are timeconsuming ones, and the optimization stage requires repeated solution of these equation systems that increases the time on development. To shorten the computation time, various methods can be applied, among them preliminary (estimated) calculation or oversimple models calculation, however, while testing and optimizing the full model is used. Reduced order models are very popular in solving this problem. The main idea of a reduced order model is to find a simplified model that may reflect the required properties of the original model as accurately as possible. There are many methods for the model order reduction, which have their advantages and disadvantages. In this article, a method based on Krylov subspaces and SVD methods is considered. A numerical experiments is given.

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