Closed Loop speed control of BLDC Motor Drive by using classical controllers with Genetic Algorithm
Abstract
Permanent magnet brushless DC motors (PMBLDC) find broad applications in industries due to their huge power density, efficiency, low maintenance, low cost, quiet operation, compact form and ease of control. The motor needs suitable speed controllers to conduct the required level of interpretation. As with PI controller, PID controller, fuzzy logic, genetic algorithms, neural networks, PWM control, and sensorless control, there are several methods for managing the BLDC motor. Generally, speed control is provided by a proportional-integral (PI) controller if permanent magnet motors are involved. Although standard PI controllers are extensively used in industry owing to their simple control structure and execution, these controllers have a few control complexities such as nonlinearity, load disruption, and parametric variations. Besides, PI controllers need more precise linear mathematical models. This statement reflects the use of Classic Controller and Genetic Algorithm Based PI, PID Controller with the BLDC motor drive. The technique is used to regulate velocity, direct the BLDC motor drive system's improved dynamic behavior, resolve the immune load problem and handle changes in parameters. Classical control & GA-based control provides qualitative velocity reaction enhancement. This article focuses on exploring and estimating the efficiency of a continuous brushless DC motor (PMBLDC) drive, regulated as a current controller by various combinations of Classical Controllers such as PI, GA-based PI, PID Controller. The controllers are simulated using MATLAB software for the BLDC motor drive.References
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2.Miller, T.J.E. (1989) Brushless Permanent Magnet and Reluctance Motor Drives, Clarendon Press, Oxford University Press.
3.Upama Das, P.K.B., and Debnath, S. (2017) Modeling and Simulation of Open Loop Model of Brush Less DC Motor by Using MATLAB Based Software. International Journal of Electronics, Electrical and Computational System, 6.
4.Upama Das, S.D., Pabitra Kumar Biswas (2018) A Comparative Study between Load and No-Load condition of Brushless DC Motor Drives by Using MATLAB. JOURNAL OF POWER TECHNOLOGIES, 3.
5.N. Hemati, 1. S.T., and Leu, M.C. (1990) Robust nonlinear control of Brushless dc motors for direct-drive robotic applications. IEEE Trans. Ind. Electron., 37.
6.Pelczewski, P.M., and Kunz, U.H. (1990) The optimal control of a constrained drive system with brushless DC motor. IEEE Transactions on Industrial Electronics, 37 (5), 342–348.
7.Ang, K.H., Chong, G., and Li, Y. (2005) PID control system analysis design, and technology. IEEE Transactions on Control Systems Technology, 13 (4), 559–576.
8.Tom O’Mahony, C.J.D.K.F. (2000) Genetic Algorithms for PID Parameter Optimisation: Minimising Error Criteria. Conference: Process Control and InstrumentationAt: University of StrathclydeVolume: pp.148 153.
9.Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc.75 Arlington Street, Suite 300 Boston, MAUnited States.
10.Pillay, P., and Krishnan, R. (1988) Modeling of permanent magnet motor drives. IEEE Transactions on Industrial Electronics, 35 (4), 537–541.
11.Krishnan, R. (2001) Electric Motor Drives Modeling, Analysis, and Control, Prentice-Hall International Inc New Jersey.
Published
2020-06-16
How to Cite
DAS, Upama; BISWAS, Pabitra Kumar.
Closed Loop speed control of BLDC Motor Drive by using classical controllers with Genetic Algorithm.
Journal of Power Technologies, [S.l.], v. 100, n. 2, p. 161-170, june 2020.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/1620>. Date accessed: 21 nov. 2024.
Issue
Section
Electrical Engineering
Keywords
BLDC motor, closed-loop control, conventional controllers, Genetic Algorithm
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