A novel control technique for torque ripple minimization in switched reluctance motor through destructive interference

Labiod, Chouaib; Srairi, K.; Mahdad, Belkacem; Benbouzid, Mohamed

doi:10.1007/s00202-017-0521-7

Cited by 16 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…= ∆ ∆ = 2 = 2 (21) In reference [17], it is emphasized that the torque slope, the motor speed, rotor and stator fluxes, stator voltage are functions of load. The expressions for the increasing in torque(T SLP + ) and the decreasing in torque (T SLP -) are given in the equations (22) and (23). The absolute slope of the T SLP should not exceed the absolute slope of the triangular carrier signal.…”

Section: Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Layermentioning

confidence: 99%

“…The slope of the T SLP in the neuro-fuzzy controller is determined by the scaling factor K SF . On the other side, the scaling factor K SF is obtained by using equations (22) and (23) as below. In addition, a flux controller is designed using a proportional gain instead of neuro-fuzzy controller or similar torque controller.…”

Section: Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Layermentioning

confidence: 99%

See 1 more Smart Citation

Design of Neuro-Fuzzy Based Torque Controller for Torque Ripple Reduction of Asynchronous Motor

Gündoğdu

Ata

Dandil

2020

Balkan Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Among all the control methods developed for Induction Motor (IM) drivers, the hysteresis controller based Direct Torque Control (DTC) method has an important place. This control method does not require other rotor and stator parameters except the stator resistance and does not require position or velocity sensors. However, there are some disadvantages of the DTC method, such as high torque, flux and current ripples. In this study, in order to reduce the high torque ripples occurring in an induction motor that is controlled by the hysteresis controller based conventional DTC method, a simple and effective Sugeno type Neuro-Fuzzy Torque Controller (NFTC) is proposed. This proposed controller is used instead of hysteresis controller. An experimental setup consisting of 1.1 kW induction motor, current and voltage measurement, DS1103 control card and two-level voltage source inverter was installed. To evaluate the performance of the proposed controller structure, various experimental studies were performed. Results obtained from the proposed NFTC based structure and conventional hysteresis controller based DTC structures are given comparatively. By the obtained experimental results, it was confirmed that the proposed NFTC-based controller structure considerably reduced flux and torque ripples in the motor.

show abstract

Section: Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Layermentioning

confidence: 99%

Section: Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Layermentioning

confidence: 99%

Design of Neuro-Fuzzy Based Torque Controller for Torque Ripple Reduction of Asynchronous Motor

Gündoğdu

Ata

Dandil

2020

Balkan Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

show abstract

“…al. [16]. The special SRM model was implemented according to the presented strategy; the reference torque was divided into two symmetrical torque references and treated by hysteresis controller.…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Torque Ripple Minimization in Switch Reluctance Motor Drives: an ANFACO Based Control

Chaple¹,

Bodkhe²,

Daigavane³

2018

IJIES

View full text Add to dashboard Cite

Abstract:In this paper, the torque ripples in the switched reluctance motor (SRM) is constrained by utilizing an Ant Colony Optimization and an Adaptive Neuro Fuzzy Inference System (ANFACO). The control scheme is utilized for the simultaneous control of the speed and current of the SRM which fuses two controlling loops of PI controller. The external loop is governed for speed control and the internal loops is governed for current control despite the ideal decision of the turn on and turn off switching angles. With respect to, for instance, speed, current, inductance and torque, the dynamic behavior of the SRM is examined to control the speed and current which restrict the torque ripple. The gain parameters of the PI controller are ideally made as the dataset by the ant colony optimization (ACO) algorithm. The created dataset is passed to the adaptive neuro fuzzy inference system (ANFIS) for the error prediction. By then, the proposed framework is executed in the MATLAB/Simulink working stage. The execution of the proposed method is affirmed by differentiating and the current systems like ant lion optimization (ALO), non dominating sorting search algorithm (NSGA-II) and combined moth flame optimization and genetic algorithm with recurrent neural network (CMFG-RNN) procedures.

show abstract

“…Bridge converter and modified miller converter are used to minimise the torque ripple in SRM drive [16]. Destructive interference based torque ripple interference is used to eliminate torque ripple [17]. The least torque swell is achieved by differing the stator and rotor post grasp [18].…”

Section: Introductionmentioning

confidence: 99%

Hybrid intelligent controller based torque ripple minimization in switched reluctance motor drive

Pushparajesh

Nandish²,

Marulasiddappa³

2021

Bulletin EEI

View full text Add to dashboard Cite

An inherent torque ripple characterizes switched reluctance technology from conventional technology. The ultimate aim of this paper is to reduce the torque ripple of the switched reluctance motor drive using genetic neural network controller based direct torque scheme. In the proposed controller network appropriate bits of data are chosen for training and testing. The proper selection of the learning rate and momentum will help in weight adjustment. Here the error is reduced which proves that the selection of voltage vectors from the vector table is precise and its results in better torque response over a wide range of speed. The simulation results reveal that the torque ripples vary between 3.25% to 1.7% for the variation in load torque and the drive speed. The experimental results for the proposed controller reveal that the torque ripple varies between 3.7% to 2.1%. Both the simulation and hardware results illustrate the efficiency of the controller.

show abstract

A novel control technique for torque ripple minimization in switched reluctance motor through destructive interference

Cited by 16 publications

References 21 publications

Design of Neuro-Fuzzy Based Torque Controller for Torque Ripple Reduction of Asynchronous Motor

Design of Neuro-Fuzzy Based Torque Controller for Torque Ripple Reduction of Asynchronous Motor

Torque Ripple Minimization in Switch Reluctance Motor Drives: an ANFACO Based Control

Hybrid intelligent controller based torque ripple minimization in switched reluctance motor drive

Contact Info

Product

Resources

About