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Input-output pairing criterion applied in the genetic algorithm for unstable linear systems

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, a new approach towards input-output pairing for an unstable system has been proposed. First, it is demonstrated that the previous method of input-output pairing for unstable plants cannot find appropriate pairs as it only checks necessary conditions for stability and integrity. Then, a new approach using relative error matrix and genetic algorithm for finding appropriate pairs in unstable systems is proposed. As it is shown, this approach takes into consideration both static and dynamic information of plant in measuring interaction. Finally, the accuracy of proposed method is demonstrated by an example and closed loop simulation.
Rocznik
Strony
873--876
Opis fizyczny
Bibliogr. 9 poz., wykr., tab.
Twórcy
autor
  • Islamic Azad University of Qazvin, Qazvin, Barajin, Barajin Rd, Iran (Islamic Republic of)
  • Islamic Azad University of Qazvin, Qazvin, Barajin, Barajin Rd, Iran (Islamic Republic of)
Bibliografia
  • [1] E.H. Bristol, “On a new measure of interaction for multivariable process control”, IEEE Transactions on Automatic Control 11, 133–134 (1966).
  • [2] M. F. Witcher and T. J. McAvoy, “Interacting control systems: steady-state and dynamic measurement of interaction”, ISA Transactions l6, 35–41 (1977).
  • [3] Q. Xiong, W. J. Cai and M. J. He, “A practical loop pairing criterion for multivariable processes”, Journal of Process Control 15, 741‒747 (2005).
  • [4] A. Khaki-Sedigh and A. Shahmansourian, “Input-output pairing using balanced realization” Electron. Lett. 32, 2027‒2028 (1996).
  • [5] B. Wittenmark and M. E. Salgado, “Hankel-norm based interaction measure for input-output pairing”, Proc. of the 2002 IFAC World Congress, Barcelona (2002).
  • [6] M. Hovd and S. Skogestad, “Pairing criteria for decentralized control of unstable systems”, Journal of Ind. Eng. Chem. Res. 9, 2134‒2139 (1994).
  • [7] L. Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, 1991.
  • [8] D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing, Boston, 1989.
  • [9] J. Lee and T.F. Edgar, “Dynamic interaction measures for decentralized control of multivariable processes”, Ind. Eng. Chem. Res. 43, 283‒287 (2004).
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-eb7b7131-fe81-4350-959c-febb7025ef89
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