Induction motor drive's parameters identification using genetic algorithms

Author(s):  
Roman Yu. Tkachuk ◽  
Alexander S. Glazyrin ◽  
Vladimir I. Polichshuk
2021 ◽  
Author(s):  
Che-Hang Cliff Chan

The thesis presents a Genetic Algorithm with Adaptive Search Space (GAASS) proposed to improve both convergence performance and solution accuracy of traditional Genetic Algorithms(GAs). The propsed GAASS method has bee hybridized to a real-coded genetic algorithm to perform hysteresis parameters identification and hystereis invers compensation of an electromechanical-valve acuator installed on a pneumatic system. The experimental results have demonstrated the supreme performance of the proposed GAASS in the search of optimum solutions.


1998 ◽  
Vol 145 (6) ◽  
pp. 587-593 ◽  
Author(s):  
F. Alonge ◽  
F.M. Raimondi ◽  
G. Ferrante ◽  
F. D'Ippolito

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