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This paper presents the mathematical model of the brushless DC (BLDC) motor drive fed by the hysteresis current controlled inverter, which is designed using the new switching function concept. The developed simulation model is applied with the fuzzy logic controller (FLC) and adaptive neuro fuzzy inference system (ANFIS) controller as speed controllers to enhance the performance of the BLDC motor drive system. The complementary strengths of FLC and neural networks are combined together to obtain the ANFIS controller. The ANFIS controller is trained by the data of closed loop BLDC motor drive system simulated with PI controller. The ANFIS controller avoids the selection of fuzzy control rules and tuning of membership functions in the manual manner as done in FLC. A comparative study of different performance specifications is proposed between FLC and ANFIS speed controller as applied to the BLDC motor drive system. The simulation results show that the ANFIS controller is more effective as compared to FLC during most of the operating conditions considered.
C. Subba Rami Reddy. 2014. \u201cPerformance Analysis of BLDC Motor Drive using New Simulation Model with Fuzzy and ANFIS Speed Controllers\u201d. Global Journal of Research in Engineering - F: Electrical & Electronic GJRE-F Volume 14 (GJRE Volume 14 Issue F4): .
Crossref Journal DOI 10.17406/gjre
Print ISSN 0975-5861
e-ISSN 2249-4596
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Total Score: 102
Country: India
Subject: Global Journal of Research in Engineering - F: Electrical & Electronic
Authors: C. Subba Rami Reddy, M. Surya Kalavathi (PhD/Dr. count: 0)
View Count (all-time): 189
Total Views (Real + Logic): 4638
Total Downloads (simulated): 2392
Publish Date: 2014 06, Sun
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This paper presents the mathematical model of the brushless DC (BLDC) motor drive fed by the hysteresis current controlled inverter, which is designed using the new switching function concept. The developed simulation model is applied with the fuzzy logic controller (FLC) and adaptive neuro fuzzy inference system (ANFIS) controller as speed controllers to enhance the performance of the BLDC motor drive system. The complementary strengths of FLC and neural networks are combined together to obtain the ANFIS controller. The ANFIS controller is trained by the data of closed loop BLDC motor drive system simulated with PI controller. The ANFIS controller avoids the selection of fuzzy control rules and tuning of membership functions in the manual manner as done in FLC. A comparative study of different performance specifications is proposed between FLC and ANFIS speed controller as applied to the BLDC motor drive system. The simulation results show that the ANFIS controller is more effective as compared to FLC during most of the operating conditions considered.
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