Model order reduction based on discrete-time Laguerre functions for discrete linear periodic time-varying systems

Sun, Lili; Xu, Kang-Li; Jiang, Yao‐Lin

doi:10.1177/0142331220949733

Cited by 8 publications

(2 citation statements)

References 29 publications

(29 reference statements)

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“…This approach is followed by matching Markov parameters and then the time moments of interval system for reduced and higher order model. Sun et al [32] proposed a new model for reducing the linear periodic time-varying systems. In this model, the technique of state space realization has been applied in the form of Fourierlifted.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Model Order Reduction in Discrete Time System

Hasan¹,

Jaafar²,

Shaker³

2021

IJIES

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In modern digital control system such as model reference control and model predictive control, where the real time calculation is the main challenge, the model order reduction has become very important issue to minimize the execution time. In this work, our aim is to construct a novel technique for reduction of high order discrete time systems. This could be achieved by computation algorithm model from a given high order pulse transfer function. The proposed model is based on matching the weighting sequence of the original parameters with those adopted in the low-order model. The generalized least squares method is then used to determine the reduced model parameters. The efficiency of the proposed algorithm is validated by using the integral squared error minimization between the original system and the reduced model. An example is presented and discussed to validate the efficiency of the proposed low-order model. Performance comparisons with many recent related works showed that the proposed model is promising in terms of low error indices and time responses.

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Section: Introductionmentioning

confidence: 99%

A Novel Approach for Model Order Reduction in Discrete Time System

Hasan¹,

Jaafar²,

Shaker³

2021

IJIES

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“…Several model abatement methods have been proposed for the approximation of large-scale systems to lower order models (Chen et al, 1980a; Desai and Prasad, 2013; Haider et al, 2019; Kumar et al, 2016; Prajapati and Prasad, 2019a, 2019b, 2019b; Shamash, 1974; Sun et al, 2020; Vasu et al, 2019). The approximation of the large-scale system is carried out in such a way that it retains the essential characteristics of the original system (Ghafoor and Imran, 2017; Haider et al, 2019; Vijaya Anand et al, 2018; Vishwakarma and Prasad, 2008).…”

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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