Reduced order modelling of linear multivariable systems using particle swarm optimisation technique

Parmar, Girish; Mukherjee, S.; Prasad, Rajendra

doi:10.1504/ijica.2007.016794

Cited by 19 publications

(19 citation statements)

References 29 publications

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“…Parmar and Mukherjee [20] used particle swarm optimization method for deriving reduced order models of linear dynamic systems. Vishwakarma and Prasad [21] used clustering method for deriving reduced order models of linear dynamic systems.…”

Section: A Transfer Function Based Methods (Classical Control Methodmentioning

confidence: 99%

Reduced order linear functional observers for large scale linear discrete-time control systems

Vaidyanathan

Madhavan

2013

2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)

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For the last five decades, there has been great interest in the control literature in deriving reduced order models for large-scale linear control systems. Such reduced order models have great applications in science, engineering and industry. In this paper, we have studied the problem of designing linear functional observers for large-scale linear discrete-time control systems and derived some new results, viz. necessary and sufficient conditions for the reduced order linear functional observer for discrete-time linear control systems. We have also established a separation principle for implementation of an observer-based feedback stabilization scheme. A simple algorithm has been provided for the ready implementation of the proposed linear functional observer. A numerical example using MATLAB has been provided to illustrate the new design procedure.

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Section: A Transfer Function Based Methods (Classical Control Methodmentioning

confidence: 99%

Reduced order linear functional observers for large scale linear discrete-time control systems

Vaidyanathan

Madhavan

2013

2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)

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“…a) Eigen spectrum zone(ESZ) of higher order system [18] [21] b) Eigne spectrum zone of lower order system [18] [21] c) Eigen spectral point of lower order system [18][21] Fig. 1.…”

Section: Model Order Reduction Proceduresmentioning

confidence: 99%

Eigen spectrum based moment matching technique for model order reduction

Lavania

Nagaria

2015

2015 39th National Systems Conference (NSC)

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In this paper a mixed approach for model order reduction is proposed. The proposed approach combines eigen spectrum analysis and Padé based moment matching technique for model order reduction. The denominator of the higher order system(HOS) is derived using eigen spectrum analysis whereas numerator dynamics are determined using Padé based moment matching technique which utilizes both time moment and Markov parameters. The efficiency and powerfulness of the method is evaluated in terms of a performance index which is known as integral square error(ISE). This method guarantees stability if the original system is stable. I. INTRODUCTIONIn the analysis of complex and intricate systems, it is often required to simplify the system for the ease of analysis, simulation and implementation. When mathematical description of any higher order system (HOS) is presented, several considerations and assumptions are made. Moreover, consideration of all aspects would make the mathematical description of the system more complex to analyze. This has preceded model order reduction (MOR) a necessary tool. Concept behind the model order reduction is to replace the higher order system with an approximated system, which provides ease of simulation, analysis and better control. Further, such a system has reduced dimensions, which significantly reduces the simulation and design time. Additionally it must be ascertained that the characteristics of the reduced order system remain pristine. The area of model order reduction is very vast and has always attracted researchers to extend the previous literature or to propose new methods. Model order reduction has been broadly classified in two domains i.e. Time domain and Frequency domain. A wide variety of techniques have been proposed in both the domains of model order reduction [1][2][3][4][5]. In time domain Singular method,Modal method,Optimal Solution etc. have been proposed [5][6][7][8][9][10]. Whereas, in frequency domain Padé approximation, Routh Approximation, Continued Fraction etc. have been proposed [11][12][13][14].

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“…In recent decades, evolutionary techniques such as particle swarm optimization and the genetic algorithm have been used for order reduction of systems [27][28][29][30]. In these approaches, the reduced-order model's parameters are achieved by minimizing a fitness function, which is often the integral square error (ISE), integral absolute error, H 2 norm, or H ∞ norm [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective weighted sum approach model reduction by Routh--Pad\'{e} approximation using harmony search

Soloklo¹,

Farsangi²

2013

Turk J Elec Eng & Comp Sci

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Abstract:A new weighted-sum multiobjective approach is investigated for order reduction based on Routh-Padé approximation, in which the harmony search algorithm is used to optimize the reduced-order model's parameters. In this method, apart from minimizing the errors between a set of subsequent time moments/Markov parameters of the system and those of the model, the error between the singular values of the reduced-order system and those of the original system is minimized. The Routh criterion is applied for specifying the stability conditions. The stability condition is then considered as a constraint in the optimization problem. To present the ability of the proposed method, 3 test systems are reduced. The results obtained show that the proposed approach performs well.

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Reduced order modelling of linear multivariable systems using particle swarm optimisation technique

Cited by 19 publications

References 29 publications

Reduced order linear functional observers for large scale linear discrete-time control systems

Reduced order linear functional observers for large scale linear discrete-time control systems

Eigen spectrum based moment matching technique for model order reduction

Multiobjective weighted sum approach model reduction by Routh--Pad\'{e} approximation using harmony search

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