Model reduction using the Routh stability criterion

Krishnamurthy, Vikram; Seshadri, V.

doi:10.1109/tac.1978.1101805

Cited by 234 publications

(52 citation statements)

References 4 publications

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“…Method 1:( Model Reduction Using the Routh Stability Criterion by V. Krishnamurthy and V. Seshadri) [3] Let the transfer function of the high-order system be…”

Section: Techniques Of Reducing Modelmentioning

confidence: 99%

“…Reduced order modeling is an important issue in control systems. In literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], various research papers have been published on reduced order modeling of higher order systems.…”

Section: Introductionmentioning

confidence: 99%

“…The first paper on reduced order modeling using Routh criterion is appeared in 1975 by V. Krishnamurthy and V. Seshadri. This technique improves the drawbacks of Pade approximation method [3]. In this method, both the poles and zeros of the reduced model are considered to be free parameters and are obtained by minimizing the integral square error in impulse or step responses.…”

Section: Introductionmentioning

confidence: 99%

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Performance Comparison of Different Type of Reduced Order Modeling Methods

Lodhwal

Jha

2013

2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT)

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Three techniques for constructing reduced order models are presented and compared. The techniques are based on: 1) Model Reduction Using the Routh Stability Criterion by V. Krishnamurthy and V. Seshadri.; 2) Higher Order Reduction by Coefficient Comparison by T. Manigandan and N. Devarajan 3) Clustering method for reducing order of linear system using Pade approximation by C.B. Vishwakarma and R. Prasad . In this paper, we have compared the reduced transfer functions obtained from all the three methods and thereafter computed the Relative Integral Square Error (RISE) for different numerical examples by using the above mentioned methods.

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“…Method 1:( Model Reduction Using the Routh Stability Criterion by V. Krishnamurthy and V. Seshadri) [3] Let the transfer function of the high-order system be…”

Section: Techniques Of Reducing Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Comparison of Different Type of Reduced Order Modeling Methods

Lodhwal

Jha

2013

2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT)

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“…There are several classical methods in literature for model reduction given by Krishnamurthy and Seshadri [1], Hutton and Friendland [2], Heydari and Pedram [3], Soloklo and Farsangi [4], etc., and based on particle swarm optimization (PSO) [5] and differential evolution (DE) [6].…”

Section: Introductionmentioning

confidence: 99%

Application and Comparative Analysis of Various Classical and Soft Computing Techniques for Model Reduction of MIMO Systems

Salma

Vaisakh

2015

Intell Ind Syst

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Model reduction techniques are simplification methods based on mathematical approaches employed to realize reduced models for the original high order systems. Some existing classical model reduction techniques for multivariable system are considered and compared for their performances. Interlacing property and coefficients matching (IPCM) method gives overall minimum integral square error (ISE), integral absolute error (IAE) and integral time absolute error (ITAE) values compared to other methods. Though the IPCM method is efficient, it may not guarantee for minimization of all objective functions simultaneously. In this paper, model reduction approach based on objectives like ISE, IAE and ITAE using multi-objective differential evolution (MODE) method is proposed for reducing the numerator and the denominator is reduced by interlacing property. MODE method minimizes the small, normal and large errors persisting for long time between original and reduced models. This multi-objective approach is applied for model reduction of 10th order multivariable linear time invariant power system model. Simulation results are demonstrated for single and multi-objective model reduction and compared with multiobjective particle swarm optimization (MOPSO) method to prove the validity of proposed MODE technique.B U. Salma

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“…Over the years, analysis, stability and transients of interval systems have attracted the Considerable attention can be observed by the available amount of literature, commencing from the basics of aggregation method [1] for reduced order model. Some other classical reduction methods include moment matching, Pade approximation and Continued fraction expansion methods [2][3][4][5][6][7][8][9] and many more. These methods do not guarantee stability, was the only drawback.…”

Section: Introductionmentioning

confidence: 99%