Adaptive Neuro-Fuzzy Inference System Based Field Oriented Control of PMSM &amp; Speed Estimation

Salem, Waleed A.A.; Osman, Gomaa Fahmy; Arfa, Shawki Hamed

doi:10.1109/mepcon.2018.8635179

Cited by 17 publications

(4 citation statements)

References 11 publications

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“…ANFIS is a fuzzy system that simplifies the complexities of the control system through data processing and is based on the concept of Artificial Neural networks (ANN). In ANFIS, a fuzzy inference mechanism by structure and an advanced adaptive NN feeding neuron are embedded [11][12][13][14][15][16][17][18][19][20][21][22][23]. The ANFIS system was used to increase the quality of control and thus increase efficiency without the need to know the internal details such as its control unit's parameters and feedback parameters.…”

Section: Pi and Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Adaptive Neuro-Fuzzy Voltage Control for LCL-Filter Grid-Connected Converter

Olwie¹,

Humod²,

Hasan³

2022

ETJ

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 This paper presents how voltage is affected by increased loads or voltage sag.  The Adaptive neuro-Fuzzy Inference System (ANFIS) is used as an Intelligent controller.  The mathematical models for the LCL filter are investigated.  The proposed approach shows more effective results than previous performance for voltage controlling and harmonic reduction.Inductance -Capacitance -Inductance (LCL) filter is a very attractive candidate for renewable energy system applications due to its high efficiency. High attenuation of the switching frequency harmonics, small size, low fee, and improving the overall harmonic distortion (THD). This paper presents how voltage is affected by increased loads or voltage sag. Therefore it is necessary to control it with certain controllers. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as an intelligent controller, the voltage constraint as training data for ANFIS obtained from PI. The filter works in a good connection between the inverter and the grid and rewords unwanted harmonics from using the inverter. The mathematical models for the LCL filter are investigated. The proposed approach shows more effective results than previous performance for voltage controlling and harmonic reduction. It gives overshoot (0.5%), steady state error (0.005), settling time (0.03 sec), rise time (0.005 sec), and improving THD 8.67% to 2.33% by comparing these results of ANFIS respectively with the results of PI which gave(3%),(0.01),(0.2sec)and( 0.02sec).

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Section: Pi and Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Adaptive Neuro-Fuzzy Voltage Control for LCL-Filter Grid-Connected Converter

Olwie¹,

Humod²,

Hasan³

2022

ETJ

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“…This hybrid mixture enables the complexities of the control system to be minimized. In ANFIS, the fuzzy inference mechanism was involved through the structure and neurons of the feed forward adaptive NN [25]- [13]. The concept of the key investigation is to utilize ANFIS to enhance the efficiency of the system and improve the controller design process.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…Speed response of AFPMSM are presented at different load s, no-load at starting and after 1sec the load is applied (10N.m), is illustrated in Figure (13). Also, table 5 records the performance constraints of the four methods.…”

Section: The Multi-objective Cost Function Of the Pi-pso To Enhance Speed And Torquementioning

confidence: 99%