Dynamic Model Identification of BLDC Motor Using Artificial Intelligence Techniques
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Abstract
This paper presents the model of brushless direct current motor including the commercial driver board. Generally, the motor speed is controlled by setting the desired conditions. The accurate model for the controller design is very important. However, the exact mathematical model is very complicated. As a result, this paper presents the black-box model to explain the response of the brushless direct current motor driven by the commercial driver board. The identification of the proposed model is necessary to search the transfer function coefficients. Therefore, this paper presents the artificial intelligence techniques to identify the black-box model represented by the transfer function. The proposed genetic algorithm (GA), particle swarm optimization (PSO) and adaptive tabu search (ATS) will tune the unknown parameters until the minimum fitness value is obtained. It can be found that the proposed methods can provide the accurate dynamic model. The performance of the presented methodology is verified by the comparison results of the speed responses from the proposed model and experimentation. The results can show that the proposed technique can provide the accurate model useful for the controller design.
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References
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