Optimizing BLDC motor drive performance using particle swarm algorithm-tuned fuzzy logic controller

Mi, Qingtao; Cao, Weifeng; Zhou, Lintao

doi:10.1007/s42452-022-05179-6

Cited by 5 publications

(2 citation statements)

References 28 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…J. Kennedy and R.C. Eberhart presented the PSO algorithm for the first time in 1995 [16], [17]. Then, in 1998, inertia weight, a new parameter was included to this algorithm for the purpose of enhancing the performance of PSO itself.Many of the researches carried out in the field of science, technology and engineering have used PSO algorithm [18].The particles search spaces in certain velocities depending on the other particles of the group.…”

Section: Figure1 Schematic Diagram Of Bldc Motormentioning

confidence: 99%

Application of Particle Swarm Optimization (PSO) Algorithm for PID Parameter Tuning in Speed Control of Brushless DC (BLDC) Motor

Bhandari,

Pancha,

Poudel

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

With the advancement in the technology of power electronics and microelectronics, the use of brushless DC motor has been increasingly expanded. The numerous benefits of brushless DC motor are one of the prime factors that has helped in the rapid expansion in the use of BLDC motor. Simple operation and maintenance, compact volume, light weight, energy conservation, high efficiency, easy regulation of speed and high reliability are some the merits that promotes the increasing use of BLDC motor. Its current application is observed in almost all the sectors of industrial control. BLDC motor drivers are most widely used in motion control applications. With the increasing necessity of designing effective control strategy, the use of electronically commuted brushless DC motors is increasing rapidly in various industrial applications. For this purpose, cheap and efficient speed regulator is needed for the motor. Proper tuning of PID can also be one of the ways of controlling BLDCM. This paper adopts intelligent controlling technique for the reduction of torque ripple, settling time and overshoot through the use of MATLAB and SIMULINK.

show abstract

Section: Figure1 Schematic Diagram Of Bldc Motormentioning

confidence: 99%

Application of Particle Swarm Optimization (PSO) Algorithm for PID Parameter Tuning in Speed Control of Brushless DC (BLDC) Motor

Bhandari,

Pancha,

Poudel

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Refs. [19,20] use metaheuristic algorithms to optimize a fuzzy controller. Still, it needs predefined parameters for a user-determined issue.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Munciño,

Damian-Ramírez,

Cruz-Fernández

et al. 2024

Algorithms

View full text Add to dashboard Cite

Direct current motors are widely used in industry applications, and it has become necessary to carry out studies and experiments for their optimization. In this manuscript, a comparison between heuristic and metaheuristic algorithms is presented, specifically, the Steiglitz–McBride, Jaya, Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) algorithms. They were used to estimate the parameters of a dynamic model that approximates the actual responses of current and angular velocity of a DC motor. The inverse of the Euclidean distance between the current and velocity errors was defined as the fitness function for the metaheuristic algorithms. For a more comprehensive comparison between algorithms, other indicators such as mean squared error (MSE), standard deviation, computation time, and key points of the current and velocity responses were used. Simulations were performed with MATLAB/Simulink 2010 using the estimated parameters and compared to the experiments. The results showed that Steiglitz–McBride and GWO are better parametric estimators, performing better than Jaya and GA in real signals and nominal parameters. Indicators say that GWO is more accurate for parametric estimation, with an average MSE of 0.43%, but it requires a high computational cost. On the contrary, Steiglitz–McBride performed with an average MSE of 3.32% but required a much lower computational cost. The GWO presented an error of 1% in the dynamic response using the corresponding indicators. If a more accurate parametric estimation is required, it is recommended to use GWO; however, the heuristic algorithm performed better overall. The performance of the algorithms presented in this paper may change if different error functions are used.

show abstract

Improved African Buffalo Optimization-Based Takagi–Sugeno–Kang Fuzzy PI Controller for Speed Control in BLDC Motor

Subramani,

Vijayarangan,

Chenniappan

2023

Electric Power Components and Systems

View full text Add to dashboard Cite

Optimizing BLDC motor drive performance using particle swarm algorithm-tuned fuzzy logic controller

Cited by 5 publications

References 28 publications

Application of Particle Swarm Optimization (PSO) Algorithm for PID Parameter Tuning in Speed Control of Brushless DC (BLDC) Motor

Application of Particle Swarm Optimization (PSO) Algorithm for PID Parameter Tuning in Speed Control of Brushless DC (BLDC) Motor

Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor

Improved African Buffalo Optimization-Based Takagi–Sugeno–Kang Fuzzy PI Controller for Speed Control in BLDC Motor

Contact Info

Product

Resources

About