A novel model reduction approach for linear time-invariant systems via enhanced PSO-DV algorithm and improved MPPA method

Vasu, Ganji; Mangipudi, Sivakumar; Ramalingaraju, M

doi:10.1177/0959651819849372

Cited by 11 publications

(10 citation statements)

References 28 publications

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“…Example 5: An uncontrolled two-area thermal-thermal (nonreheated) power system bearing the symmetric transfer function, taken from Vasu et al (2020b) Taking 𝜀 = 0.9 with 250 iterations, the total simulation time taken by the proposed methodology is 59.48 seconds, thus taking an average time of only 14.87 seconds for optimizing one subsystem. The final 3 rd order reduced system for the original 7 th order MIMO system is given as:…”

Section: Reduction Of Multi-input-multi-output Systemsmentioning

confidence: 99%

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Dynamic Boundary Conditions based Optimization Approach for Efficient Model Order Reduction of Complex Systems

Goel,

Manocha

2024

Preprint

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This paper proposed a new methodology to address the key problem in model order reduction methods of setting random values of lower & upper bounds and initial values of the parameters in optimization-based approaches. The moth flame optimization (MFO) method is utilized for the model order reduction process wherein the search space boundaries are found using a novel strategy with the classical balanced truncation technique. Both the numerator and denominator coefficients of the desired reduced-order system are found using the proposed optimization approach. The integral square error (ISE) is employed as the objective function in the optimization of SISO systems while a novel objective function is framed using ISE for the MIMO systems. The key advantage of using balanced truncation-based search space boundaries ensures targeted search with potential solutions and stability of the reduced order model. Further, the disadvantage of steady-state error of the balanced truncation is overcome using a gain adjustment factor. The overall methodology takes very less simulation time while keeping all the necessary parameters of the reduced-system close to those of the original system. To test the efficacy of the proposed methodology, five real-world high-order systems with two SISO systems, two MIMO systems and one discrete-time system are considered and compared with existing methods through several error indices and time and frequency-domain specifications. It has been found that the proposed methodology results in significant reduction of ISE and improvement in matching of step responses, preserving stability of the reduced-order models.

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Section: Reduction Of Multi-input-multi-output Systemsmentioning

confidence: 99%

“…The reduced order models obtained by Gupta & Manocha (2021) and Vasu et al (2020b) are given in Table 13. The step responses of the reduced systems are compared with that of original system in Fig.…”

mentioning

confidence: 99%

Dynamic Boundary Conditions based Optimization Approach for Efficient Model Order Reduction of Complex Systems

Goel,

Manocha

2024

Preprint

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“…Therefore, when the PSO algorithm is applied, a certain degree of improvement needs to be made to avoid its early phenomenon and fall into the optimal local value. Various investigations have been carried out, and different methods have been proposed to simplify the higher-order transfer function [5][6]. Each method has its advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

An Improvement in LQR Controller Design based on Modified Chaotic Particle Swarm Optimization and Model Order Reduction

Abdullah¹

2021

IJIES

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The diverse engineering and scientific applications are stated through complex and high-order systems. The significant difficulties of these systems are the complications of modeling, analyzing, and controlling. It is easier to examine simpler models for more physical insights than more complex models and result in lower-ordering controllers that are easier to implement. The model order reduction (MOR) was used to simplify the computational difficulty of such complications and was later developed intensively for use with increasingly CDS. In this paper, a new Modified Chaos Particle Swarm Optimization (MCPSO) technique is employed to get a reduced-order model of a large scale system and design a Linear Quadratic Regulator (LQR) based controller. The mod uses the combination of advantages of basic PSO algorithms and chaotic algorithms. It becomes an excellent algorithm with fast convergence, few control parameters, simple execution, and avoidance of local extremes. In addition to combining the chaotic algorithm, CPSO also improved the weight parameter w, adjusting it to the dynamic attenuation direction. First, efficient reduced-order model parameters are obtained for original higher-order systems based on the MCPSO. Then linear quadratic regulator (LQR (controller parameters optimized for the reduced-order model. The goodness of the proposed method is evaluated through a numerical example. The experimental results indicate that the proposed technique’s reduced order model provides an excellent close approximation to the original system.

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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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A novel model reduction approach for linear time-invariant systems via enhanced PSO-DV algorithm and improved MPPA method

Cited by 11 publications

References 28 publications

Dynamic Boundary Conditions based Optimization Approach for Efficient Model Order Reduction of Complex Systems

Dynamic Boundary Conditions based Optimization Approach for Efficient Model Order Reduction of Complex Systems

An Improvement in LQR Controller Design based on Modified Chaotic Particle Swarm Optimization and Model Order Reduction

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

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