Classic, fuzzy and predictive dtc strategies for the PMSM using the bacterial foraging algorithmas an online parameter estimator
DOI:
https://doi.org/10.17533/udea.redin.13152Keywords:
PMSM, DTC, bacterial foraging, fuzzy inference system, parameter estimation, predictive controlAbstract
This work presents a comparison between four control techniques applied to drive a PMSM: Classic DTC, Modified DTC with a Fuzzy Inference System, Predictive DTC and Predictive DTC with Fuzzy Inference System. Parameters estimation for the predictive strategies is performed using a population-based search algorithm (Bacterial Foraging), which is able to calculate on line the PMSM parameters. The electric torque and stator flux linkages experimental results show that the predictive strategies that use the machine parameters estimated by the Bacterial Foraging Algorithm present a significant improvement when compared with non predictive techniques.
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