Higher-Order Reduction of Linear System and Design of Controller

Suman, Santosh Kumar

doi:10.37575/b/eng/2235

Cited by 4 publications

(4 citation statements)

References 42 publications

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“…The performance of ROM obtained is also compared based on measures by calculating the performance indices, the accurateness of the proposed method which is index error between the transient sec-tions of the actual system and the ROM. performance indices error refer by [25,[57][58][59] as discussed by the following equation…”

Section: Hybrid Methods For Approximationmentioning

confidence: 99%

“…The complex design process and HOC achievements are the greatest disadvantages for these controllers. To contribute, several researchers in the literature have recently proposed a large number of order reduction techniques [20][21][22][23][24][25].Therefore, a ROC is needed to preserve all the appropriate characteristics of the HOC. ROC may lead to a reduction in computing effort, cost reduction and simulation time.…”

Section: Introductionmentioning

confidence: 99%

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Reduced order modelling and balancing control of bicycle robot

Santosh¹,

Kumar²

2021

FME Transactions

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A new result for balancing control of a bicycle robot (bicyrobo), employing reduced-order modelling of a pre-specified design controller structure in higher-order to derive into a reduced controller has been presented in this paper. The bicyrobo, which is an unstable system accompanying other causes of uncertainty such as UN-model dynamics, parameter deviations, and external disruptions has been of great interests to researchers. The controllers in the literature reviews come up with the higher order controller (HOC), the overall system becomes complex from the perspective of analysis, synthesis, enhancement and also not easy to handle it's hardware implementation. Therefore, a reduced-order pre-specified controller is developed in this work. It is effective enough to tackle unpredictable dynamics. The reduced-order controller (ROC) design is based on model order reduction (MOR) method, which is a resutl of hybridization of balanced truncation (BT) and singular perturbation approximation (SPA) approach. The reduced model so obtained, which retains DC gain as well, has been named as balanced singular perturbation approximation (BSPA) approach. It is based upon the preservation of dominant modes (i.e. appropriate states) of the system as well as the removal of states having relatively less important distinguishing features. The strong demerit of the BT method is that, for reduced-order model (ROM), steady-state values or DC gain do not match with the actual system values. The BSPA has been enabled to account for this demerit. The method incorporates greater dominant requirements and contributes to a better approximation as compared to the existing methods. The results obtained by applying proposed controller, are compared with those of the controllers previously designed and published for the same type of work. Comparatively, the proposed controller has been shown to have better performance as HOC. The performance of HOC and ROC is also examined with perturbed bicyrobo in terms of time-domain analysis and performance indices error.

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Section: Hybrid Methods For Approximationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reduced order modelling and balancing control of bicycle robot

Santosh¹,

Kumar²

2021

FME Transactions

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“…Such as a realization is called a balanced realization or inside balanced realization. Internal balancing of a given realization is the first step into a class of methods for MOR, called balanced truncation method (BTM) [32].…”

Section: Balanced Truncation Methodsmentioning

confidence: 99%

Model Reduction of Power System by Modified Balanced Truncation Method

Suman¹,

Kumar²

2020

ujca

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“…The most important problem in an appearance on the complex activities of the higher dimension system is that it occurs in many areas, including complicated transport, ecological systems, electrical power equipment, aeronautics, hydraulics Sikander et al, 2015b;Antoulas et al, 2000;Suman et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

Model Order Reduction by Using Improved Approximation Techniques

Suman¹,

Kumar²

2020

SJKFU

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A simplified approach for model order reduction (MOR) is presented in this article using the balanced singular perturbation approximation (BSPA) approach applicable to large-scale linear dynamical (LSLD) systems. The reduced system was so designed to preserve complete parameters of the original system with reasonable accuracy, employing MOR. The approach is based on the retention of the dominant states of the system and comparatively less important once. The reduced system comes from the preservation of the dominant States (say "desirable states") of the original system and thus from stability to preservation. The key demerit of the Balanced Truncation approach is that the ROM steady-state values do not correspond with the higher-order systems. This drawback has been eliminated in the proposed approach, which leads to hybridization of balanced truncation and singular perturbation approximation into a novel reduction method without the loss of retaining its dynamic behaviour. The proposed approach has been tested on LSLD systems and the results obtained show the efficacy of the approach. The methodology presented has been tested on two typical numerical examples taken from the literature review, to examine the performance, precision and comparison with other available standard order reduction methods. both the quality and the presentation of the manuscript. I would like to thank my supervisor, Dr. Awadhesh Kumar, for the patience guidance, encouragement and advice, he has provided throughout my time as his student. I have been extremely lucky to have a supervisor who cared so much about my work and who responded to my questions and queries so promptly.

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Higher-Order Reduction of Linear System and Design of Controller

Cited by 4 publications

References 42 publications

Reduced order modelling and balancing control of bicycle robot

Reduced order modelling and balancing control of bicycle robot

Model Reduction of Power System by Modified Balanced Truncation Method

Model Order Reduction by Using Improved Approximation Techniques

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