Vector Control Based Speed and Flux estimation in Switched Reluctance Motor Using ANN Controller

Babitha, S.; Kulkarni, Vikas; Koujalagi, Jyothi P

doi:10.1109/rteict46194.2019.9016823

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…3 Based on Fig. 5, considering I offset of 0.5, the ratio of the copper loss in both the driving methods can be obtained: 0.7  = (12) (12) indicates that with the given conditions, and compared with the SVPWM driving method, the flat bottom driving method reduces the copper loss by 30%. Besides the copper loss, the torque ripple is a significant indicator to evaluate the driving methods.…”

Section: Simulation and Comparisonmentioning

confidence: 99%

A flat bottom driving method for the full-bridge driven switched reluctance motor

Sun

Zhao

Chen

et al. 2023

IEICE Electron. Express

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This paper proposes a flat bottom driving method for a switched reluctance motor (SRM) by using a full-bridge power converter. Based on a ring structure, an SRM is able to be driven by a full-bridge inverter. With the proposed driving topology, by adding harmonics in the sinusoidal current waveform from the space vector PWM (SVPWM) driving method, the flat bottom current waveform can be generated for driving an SRM. According to the theoretical analysis and calculation, the flat bottom method reduces the copper loss, compared to the SVPWM driving method. The proposed driving topology and flat bottom method are tested by simulation and experimental system, of which the results are compared to that of the SVPWM driving method. The comparison shows that the flat bottom driving method contributes to the reduction of SRM copper loss, and the simulated results match the experimental results well.

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Section: Simulation and Comparisonmentioning

confidence: 99%

A flat bottom driving method for the full-bridge driven switched reluctance motor

Sun

Zhao

Chen

et al. 2023

IEICE Electron. Express

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“…Lesson from previous studies [5]- [7], many strategies is used in controlling BLDC motors, especially concerning the intelligent controller that very popular today [8]- [10]. Several researchers reported the use of Fuzzy [11], [12], Artificial Neural Network (ANN) [13]- [15], ANFIS [16], [17], and its combination [4], [18], [19]. Despite analyzing the digital values, a Fuzzy Logic Controller (FLC) is generated by interpreting the analog or continuous values of 0 and 1.…”

mentioning

confidence: 99%

Characteristic of Fuzzy, ANN, and ANFIS for Brushless DC Motor Controller: An Evaluation by Dynamic Test

Setiawan

Rudiyanto

Utomo

et al. 2021

IJIE

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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).

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Artificial Neural Network Based Speed Control of Carrier-Based SVPWM Inverter Fed Induction Motor

Moungsri

Jewajinda

Choorak

2021

2021 24th International Conference on Electrical Machines and Systems (ICEMS)

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Vector Control Based Speed and Flux estimation in Switched Reluctance Motor Using ANN Controller

Cited by 4 publications

References 5 publications

A flat bottom driving method for the full-bridge driven switched reluctance motor

A flat bottom driving method for the full-bridge driven switched reluctance motor

Characteristic of Fuzzy, ANN, and ANFIS for Brushless DC Motor Controller: An Evaluation by Dynamic Test

Artificial Neural Network Based Speed Control of Carrier-Based SVPWM Inverter Fed Induction Motor

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