Study and Comparison The Performance of Sensorless Control of PMSM Drive System | ||
| Engineering and Technology Journal | ||
| Article 15, Volume 32, Issue 10, October 2014, Pages 2528-2547 PDF (1.55 M) | ||
| DOI: 10.30684/etj.32.10A.15 | ||
| Authors | ||
| Majid K. Al-Khatat; Ghphran Taha Ahmed Abd | ||
| Abstract | ||
| Field oriented control space vector pulse width modulation (FOC- SVPWM) is one of the effective and modern methods for speed control of Permanent magnet synchronous motor (PMSM). A mathematical model and theoretical analysis of (FOC-SVPWM) driven a PMSM are presented .In this work, a control methods for PMSM using Model reference adaptive system (MRAS) are utilized to compare the performance behavior under conventional PI, Fuzzy PI, and Particle swarm optimization (PSO) control methods. Extensive simulation results are presented using MATLAB/SIMULINK program which including (SVPWM generation, inverter, PMSM, the reference frame transformation and different PI controllers) as well as the estimation method using MRAS. This work presents a comparative study to investigate the performance of PMSM based on MRAS when different load conditions are applied to PMSM and under three different controllers: the first controller is the Proportional-Integral (PI) based on classical trial and error method, the second controller is PI controller based on PSO technique for optimal gains tuning and thus improve the performance of the system. The obtained results show that an improvement in motor performance when using PI-PSO compared to classical PI controller. The third controller is Fuzzy-PI with scaling factor (gains) tuned by PSO technique. This method can improve the performance of the system compared with PI-PSO in terms of reducing steady state error, rising time, overshoot and smoother response to make this controller more robust to variation in load other than the rest motor controllers. | ||
| Keywords | ||
| Permanent Magnet Synchronous Motor; Sensorless controller; PSO; fuzzy; PI controller and Model Reference Adaptive System | ||
|
Statistics Article View: 229 PDF Download: 77 |
||