INVESTIGATION OF SPEED CONTROL PERFORMANCES OF THE FUZZY LOGIC CONTROLLERS HAVING DIFFERENT MEMBERSHIP FUNCTIONS AND INFERENCE METHODS

Alternative current motors are generally modelled with constant system parameters. However, parameters of the motor show variations during operation due to complex, unidentified and nonlinear system dynamics. Fuzzy logic controllers are widely used as a solution to overcome this problem. Because, fuzzy logic controllers don’t be affected from model uncertainties of the system to be controlled. In this study, permanent magnet synchronous motor that is one of the alternative current motors is controlled with fuzzy logic controller. All stages of the designed controllers are developed by using unique softwares instead of ready toolboxes. Triangular and trapezoidal membership functions with Mamdani and Larsen inference methods are used in the designed fuzzy logic controllers. Speed control performances of the fuzzy logic controllers have been investigated and compared with each other in the simulation studies. According to the obtained results from simulation studies, it is observed that fuzzy logic controllers designed with Larsen fuzzy inference method and trapezoidal input membership function provides better performance.

Keywords:

Permanent magnet synchronous machine, Fuzzy Logic,

PDF

___

Krishnan R. Electric Motor Drives: Modeling, Analysis, and Control, New Jersey USA:Prentice Hall, 2001.
Mademlis C, Xypteras J, Margaris N. Loss minimization in surface permanent-magnet synchronous motor drives. IEEE Transactions on Industrial Electronics, 2000; 47(1): 115-122.
Pillay P, Krishnan R. Modeling of permanent magnet motor drives. IEEE Transactions on Industrial Electronics, 1988; 35(4): 537-541.
Adhavan B, Kuppuswamy A, Jayabaskaran G, Jagannathan V. Field oriented control of Permanent Magnet Synchronous Motor (PMSM) using fuzzy logic controller. Recent Advances in Intelligent Computational Systems (RAICS), 2011. pp. 587-592.
Mishra A, Makwana J, Agarwal P, Srivastava SP. Mathematical modeling and fuzzy based speed control of permanent magnet synchronous motor drive. Conference on Industrial Electronics and Applications, 2012, pp. 2034-2038.
Maamoun A, Alsayed YM, Shaltout A. Fuzzy logic based speed controller for permanent-magnet synchronous motor drive. International Conference on Mechatronics and Automation, 2013, pp. 1518-1522.
Chung H-Y, Hou C-C, Chao C-L. Speed-control of a PMSM based on Integral-Fuzzy control. Fuzzy Theory and Its Applications (iFUZZY), 2013. pp. 77-82.
Jian Z, Xuhui W, Wenshan L, Peilei Z. Speed ripple minimization for interior-type PMSM using self-learning fuzzy control strategy. Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014. pp. 1-4.
Na R, Wang X. An Improved Vector-Control System of PMSM Based on Fuzzy Logic Controller. Computer, Consumer and Control (IS3C), 2014. pp. 326-331.
Ameur A, Kious M, Ameur F, Ameur I, Hadjadj A. Speed sensorless direct torque control of a PMSM drive based type-2 fuzzy logic stator resistance estimator and luenberger observer. Modelling, Identification and Control (ICMIC), 2016; pp. 219-224.
Ya G, Yi G. Research of PMSM fuzzy direct torque controller based on sliding mode observer. Mechatronics and Automation (ICMA), 2016. pp. 17-21.
Prasad KMA, Nair U, Unnikrishnan A. Fuzzy sliding mode control of a Permanent Magnet Synchronous Motor with two different fuzzy membership functions. International Conference on Power, Instrumentation, Control and Computing (PICC), 2015. pp. 1-6.
Litcanu M, Andea P, Flaviu Mihai FI. Fuzzy logic controller for permanent magnet synchronous machines. IEEE 13th International Symposium on Applied Machine Intelligence and Informatics, 2015. pp. 261-265.
Lazarescu E, Flaviu Mihai FI, Andea P, Mihaela FI. Speed control for a permanent magnet synchronous motor based on fuzzy logic with reduced perturbations on the supply network. Electric Power Quality and Supply Reliability, 2016.
Zadeh LA. Fuzzy Sets. Elsevier Information and Control, 1965; 8: 338-353.
Anand MS, Tyagi B. Design and Implementation of Fuzzy Controller on FPGA. International Journal of Intelligent Systems and Applications, 2012; 4(10): 35-42.
Jang J-SR, Sun C-T, Mizutani E. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, New Jersey, USA, Prentice Hall, 1997.
Blaschke F. Das Prinzip der Feldorientierung die Grundlage fur die Transvektor - Regelung von Diehfeldmaschinen, 1971; 45(10): 757-760.
Yu L, Wang C, Shi H, Xin R, Wang L. Simulation of PMSM Field-Oriented Control Based on SVPWM. Control And Decision Conference (CCDC), 2017. pp. 7407-7411.

Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering-Cover

ISSN: 2667-4211
Yayın Aralığı: Yılda 4 Sayı
Başlangıç: 2000
Yayıncı: Eskişehir Teknik Üniversitesi

Arşiv