Model Order Reduction using Routh Approximation Method, Factor Division Method and Genetic Algorithm

Author(s):  
Er. Nitin Yada ◽  
◽  
Er. Dharmendra Singh
2021 ◽  
Vol 5 (4) ◽  
pp. 267
Author(s):  
José Daniel Colín-Cervantes ◽  
Carlos Sánchez-López ◽  
Rocío Ochoa-Montiel ◽  
Delia Torres-Muñoz ◽  
Carlos Manuel Hernández-Mejía ◽  
...  

This paper deals with the study and analysis of several rational approximations to approach the behavior of arbitrary-order differentiators and integrators in the frequency domain. From the Riemann–Liouville, Grünwald–Letnikov and Caputo basic definitions of arbitrary-order calculus until the reviewed approximation methods, each of them is coded in a Maple 18 environment and their behaviors are compared. For each approximation method, an application example is explained in detail. The advantages and disadvantages of each approximation method are discussed. Afterwards, two model order reduction methods are applied to each rational approximation and assist a posteriori during the synthesis process using analog electronic design or reconfigurable hardware. Examples for each reduction method are discussed, showing the drawbacks and benefits. To wrap up, this survey is very useful for beginners to get started quickly and learn arbitrary-order calculus and then to select and tune the best approximation method for a specific application in the frequency domain. Once the approximation method is selected and the rational transfer function is generated, the order can be reduced by applying a model order reduction method, with the target of facilitating the electronic synthesis.


2018 ◽  
Vol 9 (06) ◽  
pp. 20447-20458
Author(s):  
Mohammad A. ALMa’aitah ◽  
Mohammed Al-Hattab ◽  
Mohammed I. Abuashour ◽  
Tha’er O. Sweidan ◽  
Omar M. Abdallah

Model order reduction is one of the crucial topics facing researchers nowadays. Various methods were conducted for achieving this goal. In this article, genetic algorithm (GA) with dominant poles methods are used to reduce high-order transfer functions (TFs) to lower-order ones. Genetic algorithm is powerful technique used for optimization purposes. In this approach, genetic algorithm is applied to model order reduction to reduce the order of the numerator of TF whereas the dominant poles method is used to reduce the order of denominator of the TF and thus improving accuracy and preserving the same dominant poles for the reduced system as the original model which are two important issues for improving the performance of simulation and computation and maintaining the behavior of the original high order models being reduced


2020 ◽  
Vol 49 (4) ◽  
pp. 20200158
Author(s):  
V. G. Pratheep ◽  
E. B. Priyanka ◽  
S. Thangavel ◽  
K. Gomathi

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