Model order reduction using response-matching technique

Mukherjee, S.; Satakshi,; Mittal, R. C.

doi:10.1016/j.jfranklin.2005.01.008

Cited by 112 publications

(37 citation statements)

References 10 publications

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“…Table 4 presents the GA second-order reduced model along with other reduced models of different reduction methods. In addition, step responses of the full-order and reduced-order models corresponding to the proposed and the recently published work of Parmar et al [16] and Mukherjee et al [37] are shown in Figure 8. The superiority of the new method can be clearly seen in Table 4 and Figure 8, where the ISE is much less and the response is much closer to the fullorder model than all of the others.…”

Section: Examplementioning

confidence: 90%

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Genetic algorithm approach with frequency selectivity for model order reduction of MIMO systems

Alsmadi

Abo-Hammour

Al-Smadi

et al. 2011

Mathematical and Computer Modelling of Dynamical Systems

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A novel genetic algorithm (GA) approach with frequency selectivity advantage for model order reduction (MOR) of multi-input-multi-output (MIMO) systems is presented in this article. Motivated by singular perturbation and other reduction techniques, the new MOR method is formulated using GAs, which can be applied to single-input-single-output (SISO)-or MIMO-type systems. The GA procedure is based on maximizing the fitness function corresponding to the response deviation between the full-order model and the reduced-order model with the option of substructure preservation. The proposed GA-MOR method is compared to the well-known reduction techniques, such as the Schur decomposition balanced truncation, proper orthogonal decomposition (POD) and state elimination through balancing-related frequency-weighted realization in addition to other recent methods. Simulation results validate the superiority and robustness of the new MOR technique as it can search the solution space for almost optimal solutions.

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Section: Examplementioning

confidence: 90%

“…In this final example, we will consider an eighth-order system TF that has been recently considered by different researchers [16,[37][38][39][40]. However, we will now apply the proposed GA reduction method without the restriction of retaining the system dominant frequencies in the reduced-order model.…”

Section: Examplementioning

confidence: 99%

Genetic algorithm approach with frequency selectivity for model order reduction of MIMO systems

Alsmadi

Abo-Hammour

Al-Smadi

et al. 2011

Mathematical and Computer Modelling of Dynamical Systems

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“…For the non-linear system of n RS = 4, the constitutive laws of C 1 and C 2 are set to be e = Ð f À Á 3 . C 1 and e = Ð f À Á 3 .…”

Section: Illustrative Example Of Eo-msamentioning

confidence: 99%

An element-oriented model simplification algorithm based on dynamic similarity

Hung

Hong

2011

Proceedings of the Institution of Mechanical Engineers, Part I:

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This paper presents an element-oriented model simplification algorithm (EO-MSA) in multi-physical domains. It aims to generate a simplified model to release computational loading while maintaining model fidelity. The dynamics of the multi-physical system can be constructed based on a unified bond graph approach. This EO-MSA approach eliminates those bond graph elements which influence the system behaviour insignificantly in the mathematical model. This study proposes two newly-defined indices, dynamic similarity (DS), and model similarity index (MSI), to evaluate the similarity of candidates which are order reduced. Ten error-dynamics modules are mathematically constructed and structurally classified for both the DS and the MSI. The reduced model with minimum MSI can be determined using this approach. Quarter-car simulation results demonstrate that this EO-MSA approach is capable of reducing system complexity while maintaining fidelity with satisfactory tolerance.

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“…The Model order reduction techniques for both type of reduction have been proposed by several researchers. A large number of methods are available in the literature for order reduction of linear continues systems in time domain as well as in frequency domain [1][2][3][4][5][6][7]. The extension of single input single output (SISO) methods to reduce multi input multi output (MIMO) systems has been also carried out in [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%