Abstract
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 using an enhanced multiresolutional dynamic genetic algorithms (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 multiresolutional dynamic adaptation algorithm and 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 directly taken from the continuous plant, instead of the exact discrete-time model, so that computation time is saved.
| Original language | English |
|---|---|
| Pages (from-to) | 280-285 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
| Volume | 1 |
| DOIs | |
| Publication status | Published - 2001 Jan 1 |
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ASJC Scopus subject areas
- Control and Systems Engineering
- Hardware and Architecture
Cite this
Discrete-time model reduction of sampled systems using an enhanced multiresolutional dynamic genetic algorithm. / Hsu, Chen-Chien James; Tse, Kai Ming; Wang, Wei-Yen.
In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vol. 1, 01.01.2001, p. 280-285.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Discrete-time model reduction of sampled systems using an enhanced multiresolutional dynamic genetic algorithm
AU - Hsu, Chen-Chien James
AU - Tse, Kai Ming
AU - Wang, Wei-Yen
PY - 2001/1/1
Y1 - 2001/1/1
N2 - 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 using an enhanced multiresolutional dynamic genetic algorithms (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 multiresolutional dynamic adaptation algorithm and 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 directly taken from the continuous plant, instead of the exact discrete-time model, so that computation time is saved.
AB - 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 using an enhanced multiresolutional dynamic genetic algorithms (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 multiresolutional dynamic adaptation algorithm and 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 directly taken from the continuous plant, instead of the exact discrete-time model, so that computation time is saved.
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U2 - 10.1109/ICSMC.2001.969825
DO - 10.1109/ICSMC.2001.969825
M3 - Article
AN - SCOPUS:0035726426
VL - 1
SP - 280
EP - 285
JO - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
JF - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
SN - 0884-3627
ER -