Abstract: The PMSM (Permanent Magnet Synchronous Motor) drive systems are often used in electrical drives because of their simple structures, ease of maintenance and efficiency. However, the nonlinear behaviour which arises mainly from motor dynamics and load characteristics and the presence of uncertainties make their control an extremely difficult task. So, the speed control strategy should be adaptive and robust for successful industrial applications. To handle the control issue more effectively, three artificial intelligence control strategies namely, Fuzzy Logic (FL), Artificial Neural Network (ANN) and Neuro-Fuzzy (NF) are proposed since they require only a reduced computation power, while maintaining satisfactory static and dynamic performance and a good insensitivity to perturbations and parameter uncertainties. The traditional back-propagation learning algorithm is used for training the ANN and the NF controllers. The performances of the three control strategies are investigated and compared in simulation. The results show that the intelligent controllers are reliable and highly effective in the speed control of the PMSM.
Noureddine Guersi , Messaoud Djeghaba and Messaoud Ramdani , 2007. Comparative Analysis of Intelligent Controllers for Permanent Magnet Synchronous Motor Drive Systems. Asian Journal of Information Technology, 6: 73-80.