A novel reduced order modeling of interval system using soft computing optimization approach

Anand, N. Vijaya; Kumar, Mahendra; Rao, R. Srinivasa

doi:10.1177/0959651818766811

Cited by 8 publications

(8 citation statements)

References 33 publications

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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). The approximated lower order model is known as a simplified model or reduced order model (ROM), which helps in reducing the complexity of analysis, simulation and design of controllers for the large-scale systems (Kumar and Nagar, 2013; Prajapati and Prasad, 2018a; Singh et al, 2019; Sonker et al, 2019).…”

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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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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“…To quickly and accurately solve the optimal segmentation threshold of flame image, intelligent optimization algorithm is often the best choice. Recently, particle swarm optimization (PSO) algorithm, as an intelligent bionic algorithm, has the advantages of simple application with few parameters and fast speed [17][18][19][20][21] and has been widely used in power system control, power plant energy-saving emission reduction, flame status identification and other fields. [22][23][24] For example, Mohanty and Hota 22 proposed a new hybrid chemical reaction optimization and PSO PI controller to solve the problem of automatic power generation control.…”

Section: Introductionmentioning

confidence: 99%

Furnace flame recognition based on improved particle swarm optimization algorithm

Wang

Chen

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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Industrial furnace kiln internal combustion flame directly reflects the combustion of fuel quality and stability and determines the security of the whole production process. The flame image contains many important information that cannot be observed by people’s eyes, as a result, how to effectively separate the flame image from the surrounding background by means of science and technology has the great research significance and application value. In this article, the idea of neighborhood particles is introduced into the standard particle swarm optimization algorithm, and a furnace flame recognition method is proposed based on improved particle swarm optimization algorithm. The method first uses red, green and blue color space to design the extraction model of flame image, then uses the proposed improved particle swarm optimization algorithm and Otsu algorithm to solve the optimal segmentation threshold involved in the model. Experimental results show that the proposed improved particle swarm optimization algorithm can always find the optimal segmentation threshold of the flame image within no more than 100 iterations and reduce the computation time nearly 0.01 s. Compared with the previous research results, the recognition rate of the extraction model designed in this article has been greatly improved to over 93%, which is of great value for the safe and stable operation of industrial furnaces.

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“…Later, the order reduction of higher order continuous interval systems is carried out using frequency domain reduction techniques [31] where the denominator of the model is derived from a differentiation method and the numerator is achieved using factor division, differentiation and Pade approximation methods. Another concept of order reduction of interval systems is stated depending on ISE minimization and impulse response energy using modified particle swarm optimization (PSO) algorithm by Anand et al [32].…”

Section: Introductionmentioning

confidence: 99%

Sine Cosine Algorithm Assisted FOPID Controller Design for Interval Systems Using Reduced-Order Modeling Ensuring Stability

Bokam

Patnana

Varshney³

et al. 2020

Algorithms

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The focus of present research endeavor was to design a robust fractional-order proportional-integral-derivative (FOPID) controller with specified phase margin (PM) and gain cross over frequency (ωgc) through the reduced-order model for continuous interval systems. Currently, this investigation is two-fold: In the first part, a modified Routh approximation technique along with the matching Markov parameters (MPs) and time moments (TMs) are utilized to derive a stable reduced-order continuous interval plant (ROCIP) for a stable high-order continuous interval plant (HOCIP). Whereas in the second part, the FOPID controller is designed for ROCIP by considering PM and ωgc as the performance criteria. The FOPID controller parameters are tuned based on the frequency domain specifications using an advanced sine-cosine algorithm (SCA). SCA algorithm is used due to being simple in implementation and effective in performance. The proposed SCA-based FOPID controller is found to be robust and efficient. Thus, the designed FOPID controller is applied to HOCIP. The proposed controller design technique is elaborated by considering a single-input-single-output (SISO) test case. Validity and efficacy of the proposed technique is established based on the simulation results obtained. In addition, the designed FOPID controller retains the desired PM and ωgc when implemented on HOCIP. Further, the results proved the eminence of the proposed technique by showing that the designed controller is working effectively for ROCIP and HOCIP.

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A novel reduced order modeling of interval system using soft computing optimization approach

Cited by 8 publications

References 33 publications

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

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

Furnace flame recognition based on improved particle swarm optimization algorithm

Sine Cosine Algorithm Assisted FOPID Controller Design for Interval Systems Using Reduced-Order Modeling Ensuring Stability

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