Development and Experimental Tests of a Simple Neurofuzzy Learning Sensorless Approach for Switched Reluctance Motors

Henriques, Luis Oscar de Araujo Porto; Rolim, L.G.B.; Suemitsu, W.I.; Dente, J. A.; Branco, P. J. Costa

doi:10.1109/tpel.2011.2129597

Cited by 59 publications

(9 citation statements)

References 34 publications

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“…Although high estimated accuracy is obtained, this neural network method is unsuitable for high-speed condition due to its time-consuming calculation. A neuro-fuzzy learning system is investigated in Reference [17] where the reference current and conducting phase voltage are completely utilized. Experiment result shows that the motor is operated well by using the corresponding speed and rotor position.…”

Section: Of 21mentioning

confidence: 99%

Position Sensorless Control of Switched Reluctance Motor with Consideration of Magnetic Saturation Based on Phase-Inductance Intersection Points Information

Cai

Wang

et al. 2018

Energies

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The precise estimation of position is an essential concern for the control of a switched reluctance motor (SRM). Given the prominent role of position, the promising sensorless control approach for an SRM drive should be capable of providing accurate position. An inductance-based approach has been widely applied in the position estimation. However, the estimated accuracy suffers from the magnetic saturation effect, resulting in the poor performance of sensorless operation. This paper presents a reversible synchronization of commutation with the corresponding rotor position for SRM. With the consideration of magnetic saturation, the proposed approach is taken as a suitable candidate and plays an essential role in accommodating the requirement of optimal control. The relationship between a typical inductance position and magnetic circuit saturation is investigated. The instant of the intersections is sensed by comparing the instantaneous inductance of adjacent phases. Thus, the predicted position is obtained with the information of the special point and the calculated average speed. Compared with other existing methods, the proposed approach has the certain advantages, such as the ability to update the estimated speed and position six times per electrical period, which guarantees the estimated accuracy. The proposed approach is also valid even when the motor is operated at an acceleration state and heavy load operation. In addition, the requirement of the educated inductance structure is not unnecessary, less memory space is needed in the chip, and the accumulated error is eliminated. The simulation and experimental findings demonstrate the feasibility and practicality of the proposed position estimation approach with carrying out the inertial operation, load mutation, and high-speed test.

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Section: Of 21mentioning

confidence: 99%

Position Sensorless Control of Switched Reluctance Motor with Consideration of Magnetic Saturation Based on Phase-Inductance Intersection Points Information

Cai

Wang

et al. 2018

Energies

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show abstract

“…As reported in [16], the flux linkage error caused by mutual coupling is around 7%, which is relatively small. Therefore, mutual coupling is usually neglected for flux linkage estimation and sensorless control [3][4][5][6][7][8][9][10][11][12][13][14][15]. In this article, mutual coupling is also neglected.…”

Section: Effect Of Mutual Couplingmentioning

confidence: 99%

“…Therefore, it is desirable to develop reliable and low-cost sensorless control methods. In recent years, many kinds of sensorless methods have been reported [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. The main idea of these techniques stems from the fact that the electromagnetic characteristics of an SRM, such as flux linkage, phase inductance, back-electromotive force (BEMF), etc., are the function of rotor position.…”

Section: Introductionmentioning

confidence: 99%

“…According to the difference in the way of rotor position decoding, they can be classified as lookup-table-based methods [2,3], mathematical-model-based methods [4][5][6][7], intelligent-approximation-algorithmbased methods [8][9][10][11][12][13], and observer-based methods [14,15]. An intuitive method uses lookup tables to represent the function relationship among flux linkage, phase current, and rotor position, as discussed in [2].…”

Section: Introductionmentioning

confidence: 99%

“…Intelligent-modelingbased methods, such as the fuzzy logic model and artificial neural networks, possess powerful function approximation and self-adaptive ability; thus, they are independent of parameters and disturbances. However, the complex computation process makes these methods difficult to implement in real-time control [8][9][10][11][12][13]. Note that the existence of drift in the flux-integration technique at low speeds contributes to the poor quality or stall of the flux-integration-based method at near zero or low speeds.…”

Section: Introductionmentioning

confidence: 99%

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Development and Experimental Tests of a Simple Neurofuzzy Learning Sensorless Approach for Switched Reluctance Motors

Cited by 59 publications

References 34 publications

Position Sensorless Control of Switched Reluctance Motor with Consideration of Magnetic Saturation Based on Phase-Inductance Intersection Points Information

Position Sensorless Control of Switched Reluctance Motor with Consideration of Magnetic Saturation Based on Phase-Inductance Intersection Points Information

Sensorless Control of Switched Reluctance Motor Based on Dynamic Thresholds of Phase Inductance

Design optimization of switched reluctance motors based on a novel magnetic parameter methodology

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