Improved Reduced-Order Modeling Using Clustering Method with Dominant Pole Retention

Tiwari, Sharad Kumar; Kaur, Gagandeep

doi:10.1080/03772063.2018.1465365

Cited by 28 publications

(19 citation statements)

References 32 publications

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“…The performance comparison of model reduction methods is done by calculating the various error indices known as integral square error (ISE), relative integral square error (RISE), integral absolute error (IAE) and integral time weighted absolute error (ITAE) in between the transient parts of the actual and reduced models and it is defined as follows (Prajapati and Prasad, 2018d; Sikander and Prasad, 2015; Tiwari and Kaur, 2018)…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…Example 1: In this SISO sixth-order system, the proposed MOR method is illustrated and this system has been recently considered by different researchers (Jamshidi, 1983; Soloklo and Farsangi, 2015; Tiwari and Kaur, 2018).…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…Example 2: In this example, let us consider a MIMO sixth-order system which has been considered by several researchers (Prajapati and Prasad, 2018e; Sikander and Prasad, 2015; Tiwari and Kaur, 2018).…”

Section: Numerical Experimentsmentioning

confidence: 99%

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A new model order reduction method for the design of compensator by using moment matching algorithm

Prajapati

Prasad

2019

Transactions of the Institute of Measurement and Control

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The aim of this paper is the construction of a new model reduction technique for large scale stable linear dynamic systems. It is principally focused on the dominant modes and time moments retention. This reduction implicates the translation of the overall important features confined in the large scale complete order model into the lower order system, allowing the computation of approximant denominator by using generalized pole clustering method. The approximant numerator is obtained by means of the factor division algorithm. As a result, a lower order system is obtained. To demonstrate its effectiveness, to highlight some fundamental of its features, and to accomplish its accuracy, a comparative study is done. Two standard numerical examples are taken, where approximant model computed by the proposed method is compared with the reduced order models computed from the recently proposed methods as well as well-known model reduction schemes. The paper is also emphasized on the design of compensator by using moment matching algorithm with the help of the reduced model. The design of compensator is validated and illustrated with the help of a standard numerical example taken from the literature.

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Section: Numerical Experimentsmentioning

confidence: 99%

Section: Numerical Experimentsmentioning

confidence: 99%

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A new model order reduction method for the design of compensator by using moment matching algorithm

Prajapati

Prasad

2019

Transactions of the Institute of Measurement and Control

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“…Tiwari and Kaur [27] used new indices to determine the dominancy of the transfer function poles efficiently. In this approach poles are selected in such a way that the improved dominant poles could be obtained.…”

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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“…Preservation of ROM passivity was guaranteed using the truncated BRT algorithms [7]. The pole clustering technique was employed in [8], while clustering along with the retention of dominant poles was reported in [9]. Geo-metric programming [10], the least-squares method [11], and moment matching techniques [12], [13] have been explored to solve MOR problems.…”

Section: Introductionmentioning

confidence: 99%

A Robust Frequency-Domain-Based Order Reduction Scheme for Linear Time-Invariant Systems

Mahata

Herencsár²,

Alagöz

et al. 2021

IEEE Access

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This paper presents a robust model order reduction technique with guaranteed stability, minimum phase, and matched steady-state response for linear time-invariant single-input-single-output systems. The proposed approach is generalized, allowing the designer to select any desired order of the reduced-order model (ROM). In contrast to the published literature, which primarily uses the time-domain behavior, the proposed technique utilizes the frequency-domain information of the full-order system. The suggested strategy allows the determination of the optimal ROM in a single step, simpler than the various recently reported mixed methods. The robustness is demonstrated using convergence studies and statistical measures about the final solution quality and model coefficients. The superiority over the recent literature is illustrated through four numerical examples using various time-domain and frequency response performance metrics.INDEX TERMS Frequency response, linear time-invariant system, model order reduction, reduced-order modelling, single-input-single-output system.

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Improved Reduced-Order Modeling Using Clustering Method with Dominant Pole Retention

Cited by 28 publications

References 32 publications

A new model order reduction method for the design of compensator by using moment matching algorithm

A new model order reduction method for the design of compensator by using moment matching algorithm

A Novel Approach for Model Order Reduction in Discrete Time System

A Robust Frequency-Domain-Based Order Reduction Scheme for Linear Time-Invariant Systems

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