Parameter Identification in BLDC Motor using Optimization Technique
Volume
3, Issue 2
Pages:
465-470
Year of Publication:
June, 2017
Journal of Applied Science and Engineering Methodologies
ISSN:
2395–5341
| Citation: P.Danusuya, K.Balamuruga, R.Mahalakshmi."Parameter Identification in BLDC Motor using Optimization Technique" Journal of Applied Science and Engineering Methodologies,Vol.3,No.2(2017):465-470.
BibTex
@article{2017paramet470, author = {P.Danusuya, K.Balamuruga, R.Mahalakshmi}, title = {Parameter Identification in BLDC Motor using Optimization Technique}, journal = {Journal of Applied Science and Engineering Methodologies}, issue_date = {15}, volume = {3}, number = {2}, month = {Jun}, year = {2017}, issn = {2395–5341}, url = {http://www.jasem.in/2017/32465470.html}, publisher = {Journal of Applied Science and Engineering Methodologies}, address = {Chennai, India} } |
| DOI: |
Abstract:
Brushless DC (BLDC) motor widely utilized in industrial automation, aerospace, and military appliances. The accurate model and efficiency parameter depending upon its analysis and design of the BLDC motor. The parameter identification is derived by practical mathematical model via optimisation techniques. The two optimization methods for parameter identification in BLDC, i.e. Deep neural network (DNN) and BAT algorithm are employed. The DNN and BAT optimisation technique can provide optimal BLDC model parameters. The speed, temperature, current and voltage of BLDC motor are measured using concern sensors with Arduino controller and analysed.
Keywords:Arduino, BAT algorithm, BLDC, current, DNN, speed, temperature and voltage.
Brushless DC (BLDC) motor widely utilized in industrial automation, aerospace, and military appliances. The accurate model and efficiency parameter depending upon its analysis and design of the BLDC motor. The parameter identification is derived by practical mathematical model via optimisation techniques. The two optimization methods for parameter identification in BLDC, i.e. Deep neural network (DNN) and BAT algorithm are employed. The DNN and BAT optimisation technique can provide optimal BLDC model parameters. The speed, temperature, current and voltage of BLDC motor are measured using concern sensors with Arduino controller and analysed.
Keywords:Arduino, BAT algorithm, BLDC, current, DNN, speed, temperature and voltage.
References:
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