| dc.description.abstract | Brushless DC (BLDC) motors are the most popular motors used by the industry because they are easy to
control. BLDC motors are generally controlled by artificial controls such as Fuzzy Logic Controller (FLC), Artificial
Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). However, the performance of the
BLDC control system in previous studies was compared separately with their respective parameters, making it
difficult to evaluate comprehensively. Therefore, in order to investigate the characteristic performance of Fuzzy,
ANN, and ANFIS, this article provides a comparison of these artificial controls. Two scenarios of the dynamic tests
are conducted to investigate control performance under constant torque-various speed and constant speed-various
torque. By dynamic testing, characteristics of Fuzzy, ANN, and ANFIS can be observed as real applications. The
testing parameters are: Settling Time, Overshoot and Overdamp (in the graph and average value), and then statistic
performance are: Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE),
and Mean Absolute Error (MAE). The test result in scenario 1 showed that the ANN has a better performance
compared to other controllers with the MAE, IAE, ITAE, and ISE value of 31.3003; 105.6280; 208.0630; and 5,7289
e4, respectively. However, in scenario 2, ANN only has a better performance compared to other controllers on just
a few parameters. In scenario 2, ANN is indeed able to maintain speed but it has a more ripple value than ANFIS.
Even so, the ripple that occurs in ANN does not have too much value compared to the setpoint. Therefore, the MAE
value of the ANN is smaller than the ANFIS (18.8937 of ANN and 28.4685 of ANFIS). | en_US |
