Improved Routh–PadÉ Approximants: A Computer-Aided Approach

Singh, V. P.; Chandra, Dinesh; Kar, Haranath

doi:10.1109/tac.2003.822878

Cited by 96 publications

(32 citation statements)

References 30 publications

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“…An error index ISE [15], known as integral square error in between the transient parts of original and reduced order systems, is calculated to measure the goodness of the LOS (i.e. the Downloaded by [Eindhoven Technical University] at 17:00 18 November 2014 smaller the ISE, the closer is G r (s) to G n (s)).…”

Section: Numerical Examplesmentioning

confidence: 99%

“…A large number of order-reduction methods for linear continuous systems in both the time and frequency domains are available in the literature [1][2][3][4][5][6][7][8]. Further, several methods which combine the features of two different methods have also been suggested [9][10][11][12][13][14][15][16]. Each of these methods has both advantages and disadvantages when tested on a specific system.…”

Section: Introductionmentioning

confidence: 99%

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System reduction using eigen spectrum analysis and Padé approximation technique

Parmar¹,

Mukherjee²,

Prasad³

2007

International Journal of Computer Mathematics

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A mixed method is proposed for deriving reduced-order models of high-order linear time invariant systems using the combined advantages of eigen spectrum analysis and the Padé approximation technique. The denominator of the reduced-order model is found by eigen spectrum analysis, and the dynamics of the numerator are chosen using the Padé approximation technique. This method guarantees stability of the reduced model if the original high-order system is stable. The method is illustrated by three numerical examples.

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Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

System reduction using eigen spectrum analysis and Padé approximation technique

Parmar¹,

Mukherjee²,

Prasad³

2007

International Journal of Computer Mathematics

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“…Author combined this technique with Genetic Algorithm for better an optimized result. There are so many approaches for the stable reduced order models presented in literature in SISO system or MIMO with mixed method [10][11][12][13][14][15][16][17] and allot work in time domain as well as in frequency domain with Genetic Algorithm.…”

Section: Introductionmentioning

confidence: 99%

Improved Pade-Pole Clustering Approach using Genetic Algorithm for Model Order Reduction

Ramawat¹,

Kumar²

2015

IJCA

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Reducing the order of higher order systems by mixed approach is Improved Pade-Pole clustering based method to derive a reduced order approximation for a stable continuous time system is presented. In this method, the denominator polynomial of the reduced order model is derive by improved pole-clustering approach and the numerator polynomial are obtain through Padé approximation technique and by parameter optimization by minimizing the mean square error between the time responses of the original and reduced system element through genetic algorithm. The reduced order model so obtained by improved clustering algorithm guaranteed the stability in the reduced model.

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“…The usefulness of techniques for deriving reduced order approximations of high order system has been already accepted due to the advantages of reduced computational effort and increased understanding of original system [5,6]. Consequently, a large number of time-domain (Hankel norm, balance truncation, and Krylov subspace projection) and frequency-domain (Moment matching, Padé approximation, Routh-Padé approximation, and pole clustering methods) system simplification techniques have been developed to suit the different requirements and a variety of numerical approximation are applied to obtain the reduced order model [7].…”

Section: Introductionmentioning

confidence: 99%