A comparative analysis of converters performance using various control techniques to minimize the torque ripple in BLDC drive system

• Published in: Sustainable computing informatics and systems Vol. 33; p. 100648
• Main Authors: Dasari, Murali, Srinivasula Reddy, A., Vijaya Kumar, M.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01-01-2022
• Subjects: Adaptive Neuro Fuzzy Inference System (ANFIS); BLDC Motor; Elephant Herding Optimization (EHO); FOPID (Fractional Order PID); Luo converter; Modified Landsman and Modified LUO (MLUO) converter; MRPID (multi Resolution PID); Adaptive Neuro Fuzzy Inference System (ANFIS); BLDC Motor; Luo converter; MRPID (multi Resolution PID); Elephant Herding Optimization (EHO); FOPID (Fractional Order PID); Modified Landsman and Modified LUO (MLUO) converter
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2021.100648

•The proposed method is used to hold the supported converter in place by reducing ripples by regulating the yield torque. The performance of the speed, current, and torque regulating systems is improved by modelling a BLDC motor drive system based on a modified Luo converter.•The FOPID controller is built using the hybrid approach described here to manage the BLDC engine's velocity and torque.•The ANFIS controller clearly beats various controllers under controllers regarded as operational states of the BLDC motor, and it is the best of all, according to the criteria used for comparison.•In this work, a novel technique combining Elephant Herding Optimization (EHO) and Adaptive Neuro Fuzzy Inference System (ANFIS) based on the MRPID controller scheme is suggested and compared, including EHO-ANFIS with FOPID, GA-ANFIS with PID, and PSO with PID.•Major goal is to reduce and regulate speed, current, and torque problems in BLDC motor drive systems.
Because of its high consistent quality, essential edge, high capability, rapid dynamic reaction, preservationist estimation, and cheap maintenance independently, the BLDC motor is widely utilised in military, industrial, and business applications. In comparison to conventional machines, the torque ripples of a BLDC motor are greatly impacted by the speed and transient line current in the commutation interval. This study investigates the value of torque ripple reduction in a BLDC motor drive system by looking at speed, current, and commutation problems. The FOPID and MRPID controllers were used to reduce torque ripple in BLDC motors caused by mistake tuning optimum gain controllers. The FOPID and MRPID controllers are offered for controlling the motor's current and speed. This suggested suppression circuit employs a modified Landsman converter and a modified Luo converter, with the obligation cycle adapted to get the optimal DC-bus voltage based on the BLDC motor's speed. The proposed method is used to hold the supported converter in place by reducing ripples by regulating the yield torque. The performance of the speed, current, and torque regulating systems is improved by modelling a BLDC motor drive system based on a modified Luo converter. In this work, a novel technique combining Elephant Herding Optimization (EHO) and Adaptive Neuro Fuzzy Inference System (ANFIS) based on the MRPID controller scheme is suggested and compared, including EHO-ANFIS with FOPID, GA-ANFIS with PID, and PSO with PID. The suggested EHO ANFIS technique's major goal is to reduce and regulate speed, current, and torque problems in BLDC motor drive systems. Simulation is carried out in the MATLAB SIMULINK model to achieve the goal.