Abstract: In this study, an Adaptive Neuro Fuzzy Inference System (ANFIS) controllers using error and derivative of error inputs is proposed for the speed control of a Permanent Magnet Synchronous Motors (PMSM). The PMSM is often used in electrical drives because of their simple structures, ease of maintenance and efficiency. However, the PMSM drive systems have a nonlinear characteristic arisen from motor dynamics and load characteristics. So they are required adaptive and robust speed control in industry applications. To overcome with this problem, an ANFIS is designed and adapted to the drive system. Neural Network (NN) is used to adjust input and output parameters of membership functions in the Fuzzy Logic Controller (FLC). The back propagation learning algorithm is used for training this network. Simulation and experimental results show that the ANFIS controller is reliable and high effectiveness in the speed control of the PMSM.
Cetin Gencer and Aysun Coskun, 2005. Robust Speed Control of Permanent Magnet Synchronous Motors Using Adaptive Neuro Fuzzy Inference System Controllers. Asian Journal of Information Technology, 4: 918-919.