{"title":"MIMO System Order Reduction Using Real-Coded Genetic Algorithm","authors":"Swadhin Ku. Mishra, Sidhartha Panda, Simanchala Padhy, C. Ardil","volume":51,"journal":"International Journal of Electronics and Communication Engineering","pagesStart":408,"pagesEnd":413,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/4570","abstract":"
In this paper, real-coded genetic algorithm (RCGA) optimization technique has been applied for large-scale linear dynamic multi-input-multi-output (MIMO) system. The method is based on error minimization technique where the integral square error between the transient responses of original and reduced order models has been minimized by RCGA. The reduction procedure is simple computer oriented and the approach is comparable in quality with the other well-known reduction techniques. Also, the proposed method guarantees stability of the reduced model if the original high-order MIMO system is stable. The proposed approach of MIMO system order reduction is illustrated with the help of an example and the results are compared with the recently published other well-known reduction techniques to show its superiority.<\/p>\r\n","references":"[1] S. Mukherjee and R.N. Mishra,\"Reduced order modelling of linear\r\nmultivariable systems using an error minimization technique\", Journalof\r\nFranklin Inst., Vol. 325, No. 2 , pp. 235-245,1998.\r\n[2] S. S. Lamba, R. Gorez and B. Bandyopadhyay \"New reduction\r\ntechnique by step error minimization for multivariable systems\", Int. J.\r\nSystems Sci., Vol. 19, No. 6, pp. 999-1009,1988.\r\n[3] R. Prasad, A. K. Mittal and S. P. Sharma \" A mixed method for the\r\nreduction of multivariable systems\", Journal of Institute of Engineers,\r\nIndia, IE(I) Journal-EL, Vol. 85, pp 177-181,2005.\r\n[4] 4. C. Hwang \" Mixed method of Routh and ISE criterion approaches for\r\nreduced order modelling of continuous time systems\", Trans. ASME, J.\r\nDyn. Syst. Meas. Control, Vol. 106,pp. 353-356. 1984.\r\n[5] S. Mukherjee, and R.N. Mishra, \"Order reduction of linear systems\r\nusing an error minimization technique\", Journalof Franklin Inst., Vol.\r\n323, No. 1, pp. 23-32, 1987.\r\n[6] N.N. Puri, and D.P. Lan, \"Stable model reduction by impulse response\r\nerror minimization using Mihailov criterion and Pade-s approximation\",\r\nTrans. ASME, J. Dyn. Syst. Meas. Control, Vol. 110, pp. 389-394, 1988.\r\n[7] P. Vilbe, and L.C. Calvez, \"On order reduction of linear systems using\r\nan error minimization technique\", Journal of Franklin Inst., Vol. 327,\r\npp. 513-514, 1990.\r\n[8] G.D. Howitt, and R. Luus, \"Model reduction by minimization of\r\nintegral square error performance indices\", Journal of Franklin Inst.,\r\nVol. 327, pp. 343-357, 1990.\r\n[9] Vishwakarma and Prasad, \"MIMO System Reduction Using Modified\r\nPole Clustering and Genetic Algorithm\", Hindawi Pub. Corp. Mod. and\r\nSim. in Engineering,\r\n[10] G. Parmar, R. Prasad, and S. Mukherjee, \"Order reduction of linear\r\ndynamic systems using stability equation method and GA,\"\r\nInternational Journal of Computer, Information, and Systems Science,\r\nand Engineering, vol. 1, no. 1, pp. 26-32, 2007.\r\n[11] R. Prasad and J. Pal, \"Use of continued fraction expansion for stable\r\nreduction of linearmultivariable systems,\" Journal of the Institution of\r\nEngineers, vol. 72, pp. 43-47, 1991.\r\n[12] M. G. Safonov and R. Y. Chiang, \"Model reduction for robust control: a\r\nschur relative errormethod,\" International Journal of Adaptive Control\r\nand Signal Processing, vol. 2, no. 4, pp. 259-272, 1988.\r\n[13] R. Prasad, J. Pal, and A. K. Pant, \"Multivariable system approximation\r\nusing polynomial derivatives,\" Journal of the Institution of Engineers,\r\nvol. 76, pp. 186-188, 1995.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 51, 2011"}