Application of flower pollination algorithm to parameter identification of DC motor model

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Abstract
Flower pollination algorithm (FPA) is one of the most efficient population-based nature-inspired metaheuristic optimization algorithms based on the flower pollination process of flowering plants. With Lévy distribution, the FPA can control the balance of exploration and exploitation properties with a proposed switch probability. This leads the FPA efficiently escape from local entrapment and reach global optimal rapidly. In this paper, the application of FPA to parameter identification of a direct current (DC) motor model is proposed. Under testing, the DC motor system was excited by the step input to generate the specific level of the motor speed considered as the output of the system. As results of parameter identification and validation, it was found that the FPA can provide the optimal parameters of DC motor model representing system dynamics accurately. Very good agreement between actual system dynamics behavior and model parameters obtained by the FPA is completely confirmed.