Speed control of switched reluctance motor with torque ripple reduction using non-dominated sorting genetic algorithm (NSGA-II)

Kalaivani, L.; Subburaj, P.; Iruthayarajan, M. Willjuice

doi:10.1016/j.ijepes.2013.04.005

Cited by 74 publications

(44 citation statements)

References 23 publications

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“…The optimal control signal is produced for the SRM by the proposed ANFACO system. The viability of the proposed strategy is tried and contrasted [20] and the current strategies, for example, ALO, NSGA-II and CMFG-RNN. …”

Section: Resultsmentioning

confidence: 99%

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

Chaple¹,

Bodkhe²,

Daigavane³

2018

IJIES

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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.

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Section: Resultsmentioning

confidence: 99%

“…The proportional and integral gains of both speed and current controller and turn on and turn off angles are ideally decided. These ideal blends of the gain parameters can lessen the torque ripple and henceforth enhance the execution of the SRM drive [20], …”

Section: Torque Ripple Minimization For Srm Drivementioning

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

show abstract

“…This is achieved using GRNN that is found to be suitable for the problem mentioned above [17]. Besides GRNN has accurate prediction ability, it is also suitable to represent the objective function in an optimization process.…”

Section: Interpolation Using Grnnmentioning

confidence: 99%

“…These design variations and optimization procedures attract a great attention as they could solve different machine design problems that are defiant to conventional programing techniques [17,18]. Multi-objective optimization techniques to improve the performance of BLDC motors are also discussed in the recent past [10].…”

Section: Introductionmentioning

confidence: 99%

Data Interpolation and Design Optimisation of Brushless DC Motor Using Generalized Regression Neural Network

Umadevi¹,

Balaji²,

Kamaraj³

et al. 2015

Journal of Electrical Engineering and Technology

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-This paper proposes a generalized regression neural network (GRNN) based algorithm for data interpolation and design optimization of brushless dc (BLDC) motor. The procedure makes use of magnet length, stator slot opening and air gap length as design variables. Cogging torque and average torque are treated as performance indices. The optimal design necessitates mitigating the cogging torque and maximizing the average torque by varying design variables. The data set for interpolation and ensuing design optimisation using GRNN is obtained by modeling a standard BLDC motor using finite element analysis (FEA) tool MagNet 7.1.1. The performance indices of the standard motor obtained using FEA are validated with an experimental model and an analytical method. The optimal design is authenticated using particle swarm optimization (PSO) algorithm and the performance indices of the optimal design obtained using GRNN is validated using FEA. The results indicate the suitability of GRNN as an interpolation and design optimization tool for a BLDC motor.

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“…NSGA -II is used to determine optimum values of turn -on, turn -off angles of SRM and the integral and derivative constants for the PI speed controller. Also the advantages of NSGA -II like its ease of use, rapid convergence are discussed in detail [15]. But the main drawback is that for every optimum angle the gain of PI controller has to be changed.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Operating Angle Optimization of Switched Reluctance Motor with Robust Speed Controller using PSO and GA

Rajini¹,

Balaji²,

Prabhu³

2015

Journal of Electrical Engineering and Technology

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-This paper's focus is in reducing the torque ripple and increasing the average torque by optimizing switching angles of 8/6 switched reluctance motor while implementing a robust speed controller in the outer loop. The mathematical model of the machine is developed and it is simulated using MATLAB/Simulink. An objective function and constraints are formulated and Optimum turn-on and turn-off angles are determined using Particle swarm optimization and Genetic Algorithm techniques. The novelty of this paper lies in implementing sliding mode speed controller with optimized angles. The results from both the optimization techniques are then compared with initial angles with one of them clearly being the better option. Speed response is compared with PID controller.

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Speed control of switched reluctance motor with torque ripple reduction using non-dominated sorting genetic algorithm (NSGA-II)

Cited by 74 publications

References 23 publications

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

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

Data Interpolation and Design Optimisation of Brushless DC Motor Using Generalized Regression Neural Network

A Comparative Study of Operating Angle Optimization of Switched Reluctance Motor with Robust Speed Controller using PSO and GA

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