Implementing SVPWM technique to axial flux permanent magnet synchronous motor drive with internal model current controller

Darba, Araz; Esmalifalak, Mohammad; Barazandeh, Ebrahim Sarbaz

doi:10.1109/peoco.2010.5559197

Cited by 9 publications

(8 citation statements)

References 10 publications

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“…Apart from the architectural dissimilarity from a conventional radial-flux PMSM, the mathematical modelling of a AFPMSM is essentially identical [48,49]. With reference to the schematics in Figure 4b, and under the following assumptions [13]:…”

Section: Three-phase Pmsm Modellingmentioning

confidence: 99%

Fault-Tolerant Control of a Dual-Stator PMSM for the Full-Electric Propulsion of a Lightweight Fixed-Wing UAV

2022

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The reliability enhancement of electrical machines is one of the key enabling factors for spreading the full-electric propulsion to next-generation long-endurance UAVs. This paper deals with the fault-tolerant control design of a Full-Electric Propulsion System (FEPS) for a lightweight fixed-wing UAV, in which a dual-stator Permanent Magnet Synchronous Machine (PMSM) drives a twin-blade fixed-pitch propeller. The FEPS is designed to operate with both stators delivering power (active/active status) during climb, to maximize performances, while only one stator is used (active/stand-by status) in cruise and landing, to enhance reliability. To assess the fault-tolerant capabilities of the system, as well as to evaluate the impacts of its failure transients on the UAV performances, a detailed model of the FEPS (including three-phase electrical systems, digital regulators, drivetrain compliance and propeller loads) is integrated with the model of the UAV longitudinal dynamics, and the system response is characterized by injecting a phase-to-ground fault in the motor during different flight manoeuvres. The results show that, even after a stator failure, the fault-tolerant control permits the UAV to hold altitude and speed during cruise, to keep on climbing (even with reduced performances), and to safely manage the flight termination (requiring to stop and align the propeller blades with the UAV wing), by avoiding potentially dangerous torque ripples and structural vibrations.

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Section: Three-phase Pmsm Modellingmentioning

confidence: 99%

Fault-Tolerant Control of a Dual-Stator PMSM for the Full-Electric Propulsion of a Lightweight Fixed-Wing UAV

2022

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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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“…Furthermore, the Back-EMF produced by a permanent magnet and produced by an excited coil is not different. Therefore the mathematical model is similar to the radial PMSM of the permanent Axial Flux magnet synchronous motor [19].…”

Section: Mathematical Model Of Afpmsmmentioning

confidence: 99%

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

Farhan¹,

Humod²,

Hasan³

2021

ETJ

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Implementing SVPWM technique to axial flux permanent magnet synchronous motor drive with internal model current controller

Cited by 9 publications

References 10 publications

Fault-Tolerant Control of a Dual-Stator PMSM for the Full-Electric Propulsion of a Lightweight Fixed-Wing UAV

Fault-Tolerant Control of a Dual-Stator PMSM for the Full-Electric Propulsion of a Lightweight Fixed-Wing UAV

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)

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