An efficient metaheuristic optimization based fuzzy controller for brushless DC drives lifetime expansion

Jegajothi, B.; Geethamahalakshmi, G.; Raja, Avinash Ravi; Mahendran, N.

doi:10.1016/j.matpr.2021.10.176

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…In this layer, equation ( 10), (11) states that every i th node has a corresponding function that adapts to its surroundings.…”

Section: Layer 1 Processmentioning

confidence: 99%

“…However, motor design and controller execution can still produce parasitic torque ripples. In this study, we take a methodical look at how motor controller constraints might cause torque ripples in a BLDC drive [11], [12], [13]. It explains the limitations of the current sensors and the pulse width modulation switching in the inverter [14], [15].…”

Section: Introductionmentioning

confidence: 99%

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Investigation on ANFIS-GA controller for speed control of a BLDC fed hybrid source electric vehicle

Babu,

Geetha

2024

EAI Endorsed Trans Energy Web

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The BLDC (Brushless DC Motor) is utilized in electric vehicles, space missions, and mechanical applications. Neural Network Inference System reduces torque ripple for hybrid electric vehicle (PV-Battery) along with BLDC drive to achieve efficient speed performance and stability. A hybrid input source methodology is thus put forwarded to drive the stator currents giving exactly the expressed electromagnetic torque and counter-EMF harmonics. The torque and speed control technique are directed to neural network interference system, and H6 Voltage Source Inverter (H6 VSI) drives BLDC with a gate pulse signal. We examine how an ANFIS-GA torque controller may eliminate BLDC torque ripples under uninterrupted hybrid power supply in this work. MATLAB (Simulink) results show that Genetic Algorithm (GA) improves training of ANFIS better with varying set speed conditions. The ANFIS-GA controller outperforms challenging controllers under various BLDC motor driving load conditions, proving its efficiency.

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“…In this layer, equation ( 10), (11) states that every i th node has a corresponding function that adapts to its surroundings.…”

Section: Layer 1 Processmentioning

confidence: 99%