A New Biased Model Order Reduction for Higher Order Interval Systems

Kumar, Mangipudi Siva; Begum, Gulshad

doi:10.15598/aeee.v14i2.1395

Cited by 7 publications

(7 citation statements)

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“…In order to conserve the passivity and stability qualities of actual HOS [24,25] in the decreasing order model, the properties listed below must be fulfilled.…”

Section: Problem Statementmentioning

confidence: 99%

A Novel Model Reduction Approach for Linear Time-Invariant Systems via Whale Optimization Algorithm

Nagababu¹,

Arun²,

Kumar³

et al. 2023

Atlantis Highlights in Intelligent Systems

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For the determination of accurate and stable decreasing-order model, Heuristic search method is used. Whale optimization algorithm is used to optimize stable higher order systems. By lowering the objective function (E) value, this approach builds the best reduced-order model. The First function determines measure of integral squared error between the original HOS step response and the decreasing-order model. The second term in the objective function assesses the reduced-order model's ability to retain the original system's full impulse response energy. By reducing objective function 'E', the suggested method ensures that the original system's correctness, stability, and passivity are preserved in the decreasing-order model. The method's validity is verified using eighth-order and ninth-order SISO systems. The results of integral squared error show that the suggested technique is superior to the existing decreasing methods that have been existing in literature.

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“…In order to conserve the passivity and stability qualities of actual HOS [24,25] in the decreasing order model, the properties listed below must be fulfilled.…”

Section: Problem Statementmentioning

confidence: 99%

A Novel Model Reduction Approach for Linear Time-Invariant Systems via Whale Optimization Algorithm

Nagababu¹,

Arun²,

Kumar³

et al. 2023

Atlantis Highlights in Intelligent Systems

View full text Add to dashboard Cite

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“…The MOD techniques are employed for diminution of large-scale systems in frequency-domain as well as in time-domain, both. Some of the predominant techniques are Padé approximation method [11], biased factor division method [12], Routh approximation (RA) method [13]- [15], stable approximation method [9], direct series and dominant pole method [2], [3], stability equation method [16], etc. Further, some other order diminution techniques utilized are mixed methods [4]- [9].…”

Section: Introductionmentioning

confidence: 99%

Model Order Diminution of Discrete Interval Systems Using Kharitonov Polynomials

et al. 2022

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In this research proposal, diminution of higher order (HO) discrete interval system (DIS) is accomplished by utilizing Kharitonov polynomials. The DIS is firstly, transformed into continuous interval system (CIS). The Markov-parameters (MPs) and time-moments (TMs) are exploited for determination of approximated models. The ascertainment of model order diminution (MOD) of DISs is done by Routh-Padé approximation. The Routh table is utilized to obtain the denominator of approximated model. The unknown numerator coefficients of desired approximated model are determined by matching MPs and TMs of DISs and desired model. This whole procedure of MOD is elucidated with the help of one test illustration in which third order system is reduced to first order model as well as second order model. To prove applicability of the proposed method, impulse, step and Bode responses are plotted for both system and model. For relative comparison, time-domain specifications of proposed model are tabulated for both upper and lower limits. Further, performance indices are specified for dissimilarities between responses of system and model. The obtained results depict the effectiveness and efficacy for the proposed method.

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“…Kumar et al [21] proposed uncertain parameters based method for reduction of model order, where the bounds of the uncertain parameters are initially specified. The parameters of the numerator could be obtained using one of the following methods: Pade approximation, moment matching, differentiation, or factor division.…”

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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A New Biased Model Order Reduction for Higher Order Interval Systems

Cited by 7 publications

References 0 publications

A Novel Model Reduction Approach for Linear Time-Invariant Systems via Whale Optimization Algorithm

A Novel Model Reduction Approach for Linear Time-Invariant Systems via Whale Optimization Algorithm

Model Order Diminution of Discrete Interval Systems Using Kharitonov Polynomials

A Novel Approach for Model Order Reduction in Discrete Time System

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