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Discrete-time model reduction of sampled systems using an enhanced multiresolutional dynamic genetic algorithm

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3 Author(s)
Chen-Chien Hsu ; Dept. of Electron. Eng., St. John's & St. Mary's Inst. of Technol., Taipei, Taiwan ; Kai-Ming Tse ; Wei-Yen Wang

A framework to automatically generate a reduced-order discrete-time model for the sampled system of a continuous plant preceded by a zero-order hold (ZOH) using an enhanced multi-resolution dynamic genetic algorithm (EMDGA) is proposed in this paper. Chromosomes consisting of the denominator and the numerator parameters of the reduced-order model are coded as a vector with floating-point-type components and searched by the genetic algorithm. Therefore, a stable optimal reduced-order model satisfying the error range specified can be evolutionarily obtained. Because of the use of the multi-resolution dynamic adaptation algorithm and the genetic operators, the convergence rate of the evolution process to search for an optimal reduced-order model can be expedited. Another advantage of this approach is that the reduced discrete-time model evolves based on samples taken directly from the continuous plant, instead of the exact discrete-time model, so that computation time is saved

Published in:

Systems, Man, and Cybernetics, 2001 IEEE International Conference on  (Volume:1 )

Date of Conference:

2001