Application of Parallel Flower Pollination Algorithm to Fractional-Order Model Identification of BLDC Motor

This paper proposes the newest modified version of the original flower pollination algorithm (FPA) named the parallel flower pollination algorithm (PFPA) and its application to the fractional-order (FO) mathematical model identification of the brushless DC (BLDC) motor. Based on the time sharing or the multiple point single strategy (MPSS), the proposed PFPA is suitable for use on a single CPU platform. Some results of the PFPA’s performance test over five standard benchmark functions are reported in this paper. Then, the PFPA is applied to identify the model parameters of the BLDC motor. As results, the PFPA can optimally provide the BLDC motor model parameters of both integer-order (IO) and FO models. However, the FO model performs more accurate than the IO one.