A novel improved hybrid approach for order reduction of high order physical systems

Gupta, Ankur; Manocha, Amit Kumar

doi:10.1007/s12046-021-01613-4

Cited by 11 publications

(2 citation statements)

References 46 publications

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“…To overcome this problem, methods were invented in which the reduced system also remains comparatively stable. Modified pole clustering [28][29][30] was introduced which employed GA in it forming a mixed approach of order reduction. It is more efficient as it generates dominant centers of cluster accounting for better performance.…”

Section: Introductionmentioning

confidence: 99%

Designing of 2‐degree of freedom load frequency controller for power system using novel improved pole clustering and genetic method of reduced‐order modelling

Gupta

Manocha

2021

Int Trans Electr Energ Syst

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Section: Introductionmentioning

confidence: 99%

Designing of 2‐degree of freedom load frequency controller for power system using novel improved pole clustering and genetic method of reduced‐order modelling

Gupta

Manocha

2021

Int Trans Electr Energ Syst

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“…In Sikander and Prasad (2019), the application of system diminution technique for balancing control of the two-wheeled mobile robot is illustrated. In Gupta and Manocha (2021); Kumari and Vishwakarma (2021); Prajapati and Prasad (2019a, 2020); Tong and Chen (2017); Tong et al (2014), the application of model diminution techniques is discussed in the control systems.…”

Section: Introductionmentioning

confidence: 99%

Reduction of linear dynamic systems using generalized approach of pole clustering method

Prajapati

Prasad

2021

Transactions of the Institute of Measurement and Control

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A new model order abatement method based on the clustering of poles and zeros of a large-scale continuous time system is proposed. The clustering of poles and zeros are used for finding the cluster centres. The abated model is identified from the cluster centres, which reflect the effectiveness of the dominant poles of the clusters. The cluster centre is determined by taking [Formula: see text] root of the sum of the inverse of [Formula: see text] power of poles (zeros) in a particular cluster. It is famous that the magnitude of the pole cluster centre plays an important role in the clustering technique for the simplification of large-scale systems. The magnitude of the cluster centres computed by the modified pole clustering method or some other methods based on the pole clustering techniques is large as compared to the proposed technique. The less magnitude of pole cluster centre reflects the better approximations and proper matching of the abated model with the original system. Therefore, the proposed method offers better approximations matching between actual and abated systems during the transient period compared to some other clustering methods, which supports the replacement of large-scale systems by proposed abated systems. The proposed technique is a generalized version of the standard pole clustering technique. The proposed method guarantees the retention of dominant poles, stability and other fundamental control properties of the actual plant in the abated model. The proposed algorithm is illustrated by the five standard systems taken from the literature. The accuracy and effectiveness of the proposed method are verified by comparing the time responses and various performance error indices.

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Moth Flame Optimization for Model Order Reduction of Complex High Order Linear Time-Invariant Systems

Goel,

Manocha

2024

Circuits Syst Signal Process

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A novel improved hybrid approach for order reduction of high order physical systems

Cited by 11 publications

References 46 publications

Designing of 2‐degree of freedom load frequency controller for power system using novel improved pole clustering and genetic method of reduced‐order modelling

Designing of 2‐degree of freedom load frequency controller for power system using novel improved pole clustering and genetic method of reduced‐order modelling

Reduction of linear dynamic systems using generalized approach of pole clustering method

Moth Flame Optimization for Model Order Reduction of Complex High Order Linear Time-Invariant Systems

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