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

Salma, U.; Vaisakh, K.

doi:10.1007/s40903-015-0033-6

Cited by 8 publications

(1 citation statement)

References 23 publications

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“…Among them, very few are extended for LTI multi-input multi-output (MIMO) systems. [4][5][6][7][8][9] Based upon the simplicity and the enormous design methods gathered in the frequency domain, the MOR techniques of this group have become more prominent. The frequency-domain MOR methods available in the literature are briefly classified into three categories: (1) conventional-type MOR methods, [9][10][11][12] (2) stochastic search-based MOR methods and [13][14][15][16][17][18] (3) hybridization of conventional-type and stochastic search algorithm-based MOR methods.…”

Section: Introductionmentioning

confidence: 99%

A novel model reduction approach for linear time-invariant systems via enhanced PSO-DV algorithm and improved MPPA method

Vasu

Mangipudi²,

Ramalingaraju

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, the combination of stochastic search and conventional approaches are used to develop an optimal frequency-domain model order reduction method for determining the stable and accurate reduced-order model for the stable large-scale linear time-invariant systems. The method uses the enhanced particle swarm optimization with differentially perturbed velocity algorithm to determine the denominator polynomial coefficients of the reduced-order model, whereas the numerator polynomial coefficients of the reduced-order model are determined by using an improved multi-point Padé approximation method. The method generates an optimum reduced-order model by minimizing an objective function [Formula: see text], which is formulated using two functions. The first function, [Formula: see text], evaluates the measure of integral squared error between the step responses of the original system and the reduced-order model. And the second function evaluates the measure of retention of full impulse response energy of the original system in the reduced-order model. Therefore, by minimizing the objective function ‘ E’, the proposed method is guaranteed for preserving passivity, stability and the accuracy of the original higher order system in the reduced-order model. The proposed method is extended to the linear time-invariant multi-input multi-output system. In this case, an optimal reduced-order model is determined by minimizing a single objective function [Formula: see text], which is formulated by linear scalarizing of all the objective function [Formula: see text] components. The method is popular for preserving stability, passivity and accuracy of the original system in the reduced-order model. The validation of the method is shown by applying to a sixth-order single-input single-output hydropower system model as well as to the seventh-order two-area multi-input multi-output power system model. The comparison of the simulation results of integral squared error and impulse response energy values of the reduced-order models demonstrates the dominance of the proposed method than the existing reduction methods available in the literature.

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