Parameters Identification of a Nonlinear System Based on Genetic Algorithms with an Optimized Cost Function
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This paper presents the application of genetic algorithm (GA) techniques to identify the induction motor parameters. The induction motor is a non linear system, which made the identification of it's parameters more difficult in operating mode. Genetic algorithm is one of the best soft computing techniques used for this object. In this paper, the GA cost function is the weighted sum of stator current and rotor speed errors between the plant and the model of the induction motor. The weights of the cost function are also optimized in order to improve the speed convergence of the GA. The simulation results show that the identification method based on optimized cost function of GA is feasible and gives high precision.
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