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 19 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

 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).

show abstract

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

View full text Add to dashboard Cite

show abstract

“…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%

See 1 more Smart Citation

Field Oriented Control of AFPMSM for Electrical Vehicle Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Farhan¹,

Humod²,

Hasan³

2021

ETJ

View full text Add to dashboard Cite

“…However, PID control has the drawbacks of slow response speed and weak disturbance rejection. Advanced intelligent algorithms have been incorporated into PID controllers to improve PID control effects, such as genetic algorithm PID, selftuning PID, artificial intelligence algorithm, and neural network PID [13][14][15][16]. Intelligent control algorithms have complex algorithms, high computational complexity, and pose challenges in engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Research on Friction Compensation Method of Electromechanical Actuator Based on Improved Active Disturbance Rejection Control

Zhang,

Shi,

2023

Actuators

View full text Add to dashboard Cite

The friction factor of harmonic reducers affects the transmission accuracy in electromechanical actuators (EMAs). In this study, we proposed a friction feedforward compensation method based on improved active disturbance rejection control (IADRC). A mathematical model of EMA was developed. The relationship between friction torque and torque current was derived. Furthermore, the compound ADRC control method of second-order speed loop and position loop was studied, and an IADRC control method was proposed. A real EMA was developed, and the working principles of the EMA driving circuit and current sampling were analyzed. The three methods—PI, ADRC, and IADRC—were verified by conducting speed step experiments and sinusoidal tracking experiments. The integral values of time multiplied by the absolute error of the three control modes under the step speed mode were approximately 47.7, 32.1, and 15.5, respectively. Disregarding the inertia of the reducer and assuming that the torque during no-load operation equals the friction torque during constant motion, the findings indicate that, under a load purely driven by inertia, the IADRC control method enhances tracking accuracy.

show abstract

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

Cited by 19 publications

References 11 publications

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

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

Field Oriented Control of AFPMSM for Electrical Vehicle Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Research on Friction Compensation Method of Electromechanical Actuator Based on Improved Active Disturbance Rejection Control

Contact Info

Product

Resources

About