Novel bacterial foraging-based ANFIS for speed control of matrix converter-fed industrial BLDC motors operated under low speed and high torque

Sivarani, T. S.; Jawhar, S. Joseph; Kumar, C. Agees; kumar, K. Prem

doi:10.1007/s00521-016-2652-6

Cited by 28 publications

(13 citation statements)

References 16 publications

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“…A new learning scheme based on evolutionary and swarm intelligence algorithms have been employed for improving efficiency and effectiveness of conventional neuro-fuzzy system using fuzzy linguistic hedges which employed to define the flexible shapes of the fuzzy membership functions [18]. Other evolutionary optimization methods like Bacterial Foraging Optimization Algorithm (BAOA), genetic algorithm (GA), differential evolution (DE) and simulated annealing (SA) are used in tuning the parameters of neuro-fuzzy inference systems [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Regularized extreme learning adaptive neuro-fuzzy algorithm for regression and classification

Shihabudheen

Pillai

2017

Knowledge-Based Systems

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Section: Introductionmentioning

confidence: 99%

Regularized extreme learning adaptive neuro-fuzzy algorithm for regression and classification

Shihabudheen

Pillai

2017

Knowledge-Based Systems

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“…Besides, the switched reluctance external rotor motor drive with the closed-loop rotor speed control for a fan in air conditioner was implemented using a fuzzy logic algorithm [21]. Comparative analyses between PI controller and fuzzy logic controller were performed to overcome the shortcomings of the PI controller [22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-logic-based robust speed control of switched reluctance motor for low and high speeds

Omaç¹

2019

Turk J Elec Eng & Comp Sci

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Switched reluctance motor (SRM) is operated at high magnetic saturation to generate large torque. The flux linkage of SRM is a nonlinear function with phase current and rotor position because of the high magnetic saturation. Also, the performance of the speed controller for the SRM driver system can be negatively affected by noise, disturbances, and inertia of load torque. Therefore, the fuzzy speed controller for the SRM driver system was developed in this study. In addition, a dynamic model of SRM was simulated in Matlab/Simulink software. Based on the results obtained in this study, the speed of the SRM was controlled over a wide range of speeds including low and high speeds by the fuzzy speed controller. Furthermore, in simulation, the rotor speed was simulated depending on the reference speed. Moreover, the speed of the SRM was experimentally tested using the DS1103 Ace kit. Finally, simulation results were compared with experimental results and they were found to be consistent with each other.

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“…A control based on fuzzy logic has been developed to overcome the weakness of conventional PIDs [7], but the efficiency of fuzzy control is limited because it is built on human experiences. This has led several researchers to develop modern methods aimed at improving the performance of the DC motor in order to avoid the shortcomings of conventional PIDs and the limitations of fuzzy control [8]. ANFIS, for instance, is one of the most useful techniques exploited to control DC motor speed.…”

Section: Introductionmentioning

confidence: 99%

“…inertia, resistance, inductance and magnetic flux. In a number of studies, algorithms have been developed to cope with the accelerated progression of the motor industry, where [8] and [28] developed a novel bacterial foraging and antlion algorithm to enhance ANFIS performance.…”

Section: Introductionmentioning

confidence: 99%

Study on the Extent of the Impact of Data Set Type on the Performance of ANFIS for Controlling the Speed of DC Motor

Guo

Mohamed

2019

J. Eng. Technol. Sci.

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This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for tracking SEDC motor speed in order to optimize the parameters of the transient speed response by finding out the perfect training data provider for the ANFIS. The controller was adjusted using PI, PD and PIPD to generate data sets to configure the ANFIS rules. The performance of the ANFIS controllers using these the different data sets was investigated. The efficiencies of the three controllers were compared to each other, where the PI, PD, and PIPD configurations were replaced by ANFIS to enhance the dynamic action of the controller. The performance of the proposed configurations was tested under different operating situations. Matlab's Simulink toolbox was used to implement the designed controllers. The resultant responses proved that the ANFIS based on the PIPD dataset performed better than the ANFIS based on the PI and PD data sets. Moreover, the suggested controller showed a rapid dynamic response and delivered better performance under various operating conditions.

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Novel bacterial foraging-based ANFIS for speed control of matrix converter-fed industrial BLDC motors operated under low speed and high torque

Cited by 28 publications

References 16 publications

Regularized extreme learning adaptive neuro-fuzzy algorithm for regression and classification

Regularized extreme learning adaptive neuro-fuzzy algorithm for regression and classification

Fuzzy-logic-based robust speed control of switched reluctance motor for low and high speeds

Study on the Extent of the Impact of Data Set Type on the Performance of ANFIS for Controlling the Speed of DC Motor

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