An automatic pin identification method for a three-phase DC brushless motor

Wang, Shyh-Jier; Cheng, Chi-Chang; Lin, Shir‐Kuan; Ju, Jau-Jiu; Huang, Der‐Ray

doi:10.1109/tmag.2005.854970

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Equations (4) and (5), the crossover probability will be reduced to preserve excellent chromosomes; on the contrary it will be added to evolutionary excellent chromosomes. And then, the mutation probability will be reduced to preserve excellent chromosomes; on the contrary it will increase the diversity of the population and avoid to falling into local optimum [18].…”

Section: Methods 1 Of Argamentioning

confidence: 99%

“…The conventional dc motor can be represented by the mathematical model. The technicians often wanted to test the performance or measure the parameters of a motor, and the ordinary differential equations of a motor were solved by Runge-Kutta method [4] [5]. However, the precise of a BLDC motor requires its accurate parameters, which can be measured or estimated.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Real-Coded Genetic Algorithm for Identifying Motor Systems

Fung¹,

Lin²

2015

MME

View full text Add to dashboard Cite

In this paper, the main objective is to identify the parameters of motors, which includes a brushless direct current (BLDC) motor and an induction motor. The motor systems are dynamically formulated by the mechanical and electrical equations. The real-coded genetic algorithm (RGA) is adopted to identify all parameters of motors, and the standard genetic algorithm (SRGA) and various adaptive genetic algorithm (ARGAs) are compared in the rotational angular speeds and fitness values, which are the inverse of square differences of angular speeds. From numerical simulations and experimental results, it is found that the SRGA and ARGA are feasible, the ARGA can effectively solve the problems with slow convergent speed and premature phenomenon, and is more accurate in identifying system's parameters than the SRGA. From the comparisons of the ARGAs in identifying parameters of motors, the best ARGA method is obtained and could be applied to any other mechatronic systems.

show abstract

Section: Methods 1 Of Argamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%