Abstract
Reducing the order of a system from higher to lower one is termed as model order reduction (MOR). Usual numerical algorithms are available to get reduced order model from higher order system. In this paper, the higher order associated with the system is reduced to lower one with the help of different methods. The pole clustering algorithm to develop an approximation for a stable higher order system is presented. In proposed method, denominator coefficients of reduced order model are obtained by improved pole clustering, and numerator coefficients are determined by Pade approximation technique. The second method introduced is differentiation technique, in which denominator coefficients of the reduced order model are obtained by means of differentiation technique and numerator coefficients are obtained by Routh approximation method. The genetic algorithm (GA) is also exposed in reducing the order. GA based on minimization of the integral-squared error (ISE) pertaining to a unit step input is introduced. The algorithm presented is simple and computer oriented. The reduced order model retains the stability of the system if the original higher order system is stable. A test system is given to demonstrate the superiority of order reduction by GA over some existing methods. The proposed methods are compared based on step response specifications, Bode response specifications and ISE.
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Singh, P., Dewangan, P.D. (2021). Model Order Reduction of Fixed Coefficient System Using Genetic Algorithm. In: Kumar, R., Dohare, R.K., Dubey, H., Singh, V.P. (eds) Applications of Advanced Computing in Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4862-2_11
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DOI: https://doi.org/10.1007/978-981-33-4862-2_11
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