An approach for sensorless position estimation for switched reluctance motors using artifical neural networks

Meşe, Erkan; Torrey, D.A.

doi:10.1109/63.988671

Cited by 138 publications

(56 citation statements)

References 17 publications

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“…Rule base for the sensorless scheme is made according to the number of inputs and their respective output. The rule base of fuzzy controller has been designed using flux linkage (20) and phase current (20) as inputs and angle in term of (32) output [19], [21].…”

Section: (Iv) Represent Inputs and Output Domain In Fuzzy Resignsmentioning

confidence: 99%

Sensorless Control of Switched Reluctance Motor Drive with Fuzzy Logic Based Rotor Position Estimation

Gupta¹,

Kumar²,

Bishnoi³

2010

IJCA

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This paper describes an accurate rotor position estimation, which is very important for high performance operation of Switched Reluctance Motor (SRM). Earlier, a rotor position sensor has been used for sensing the rotor position. The position sensor used in SRM drives have the disadvantages of additional cost, electrical connections, mechanical alignment problems, less suitability to space restricted applications and significant disadvantage of being inherent source of unreliability. These bottlenecks have been motivated for the development of several sensorless techniques in the recent years. Here, a proposed sensorless scheme based on fuzzy system is used to overcome the disadvantages of sensor scheme. The rotor position or angle can be estimated by using the unique relationship between flux linkage and phase current in term of fuzzy rule base. Both simulation and experiment results on a Digital Signal Processor (DSP) based real time drive are presented to show the effectiveness of this scheme. The proposed scheme is provided to demonstrate the validity for implementing it to real world problems.

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Section: (Iv) Represent Inputs and Output Domain In Fuzzy Resignsmentioning

confidence: 99%

Sensorless Control of Switched Reluctance Motor Drive with Fuzzy Logic Based Rotor Position Estimation

Gupta¹,

Kumar²,

Bishnoi³

2010

IJCA

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“…Many of these sensorless techniques use the indirect rotor position estimation such as Flux-Current Detection Technique, Current Gradient Method, Chopping Current Detection Technique [9][10][11]. Furthermore, state observer, fuzzy logic and artificial neural network are implemented with the capability of operating over a wide speed range using only terminal voltage and current of the machine [12][13][14][15][16][17]. However, these techniques have been exhibited with their advantages and drawbacks depending on the typical operating principles.…”

Section: Introductionmentioning

confidence: 99%

Rotor Position Estimation Strategy Using Artificial Neural Network for a Novel Design Transverse Flux Machine

Türker¹,

Kuyumcu²

2015

Journal of Electrical Engineering and Technology

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-The E-Core Transverse Flux Machine is a different design of transverse flux machines combined with reluctance principle. Determination of the rotor position is important for the movement of the ETFM by switching the phase currents in synchronism with the inductance regions of the stator windings. It is the first time that rotor position estimation based on Artificial Neural Network (ANN) is purposed to eliminate the position sensor for the ETFM. Simulation and experimental tests are demonstrated for the feasibility of the proposed estimation algorithm for the exercise bike application of the ETFM.

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“…In existing sensorless control methods, the main approaches can be classified as current waveform based methods [6][7][8], high frequency pulse injection methods [9][10][11], flux linkage based methods [12,13], state observer based methods [14,15], and intelligent algorithm based methods [16,17]. In [6], the current rising time during current chopping control (CCC) is regarded as a medium to reflect the variation of inductance.…”

Section: Introductionmentioning

confidence: 99%

“…To deal with the issue, a slidingmode-observer technology is employed in [14,15] for four-quadrant sensorless operation of SRMs, covering a wide speed range. Also, some intelligent techniques also are used for rotor position estimation aiming at to improve the estimation accuracy, including the neural network [16,17].…”

Section: Introductionmentioning

confidence: 99%

Sensorless Estimation of Single-Phase Hybrid SRM using Back-EMF

Tang¹,

He²,

Lee³

et al. 2017

Journal of Electrical Engineering and Technology

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-This paper presents a novel scheme to estimate the rotor position of a single-phase hybrid switched reluctance motor (HSRM). The back-EMF generated by the permanent magnet (PM) field whose performance is motor parameter independent is adopted as an index to achieve the sensorless control. The differential value of back-EMF is calculated by hardware and processed by DSP to capture a fixed rotor position four times for every mechanical cycle. In addition, to accomplish the normal starting of HSRM, the determination method of the turn-off time position at the first electrical cycle is also proposed. In this way, a sensorless operation scheme with adjustable turn on/off angle can be achieved without substantial computation. The experimental verification using a prototype drive system is provided to demonstrate the viability of the proposed position estimation scheme.

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An approach for sensorless position estimation for switched reluctance motors using artifical neural networks

Cited by 138 publications

References 17 publications

Sensorless Control of Switched Reluctance Motor Drive with Fuzzy Logic Based Rotor Position Estimation

Sensorless Control of Switched Reluctance Motor Drive with Fuzzy Logic Based Rotor Position Estimation

Rotor Position Estimation Strategy Using Artificial Neural Network for a Novel Design Transverse Flux Machine

Sensorless Estimation of Single-Phase Hybrid SRM using Back-EMF

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