Open AccessDesign methodology and experimental verification of intelligent speed controllers for sensorless permanent magnet Brushless DC motor
Author(s) -
Vanchinathan K.,
Valluvan K. R.,
Gnanavel C.,
Gokul C.
Publication year - 2021
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12991
Subject(s) - control theory (sociology) , matlab , controller (irrigation) , dc motor , electronic speed control , genetic algorithm , computer science , heuristic , engineering , control (management) , artificial intelligence , electrical engineering , machine learning , agronomy , biology , operating system
Summary This article presents a modified genetic algorithm (MGA) to determine the five degrees of freedom parameters, namely K p , K i , K d , λ , and μ of a fractional order proportional integral derivative (FOPID) controller to achieve the speed control of a brushless direct current (BLDC) motor by sensorless technique. The MGA is a meta‐heuristic inspired algorithm for solving nonlinearity problems such as sudden load disturbances, power fluctuations, and misalignment of the motor. The conventional genetic algorithm (CGA) is not very effective in solving the above‐mentioned problems. Hence, MGA‐optimized FOPID (MGA‐FOPID) controller is recommended for sensorless speed control of BLDC motor under varying load ( T L ) conditions, and varying set speed ( N s ) conditions. The proposed design of the MGA‐FOPID controller has been implemented in MATLAB 2019a with Simulink models for optimal speed control of the BLDC motor. Also, the hardware experimental set‐up and the results of the proposed controller are presented. The performance of the MGA‐FOPID controller is observed and evaluated for time‐domain characteristics. It is important to note that the proposed MGA‐FOPID controller is more effective than the MGA optimized integer order proportional integral derivative controller in terms of minimizing time integral performance indexes.