Neural networks Implementation of Direct Torque Control of Permanent Magnet Synchronous Motor

Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. This paper discusses the application of neural networks to control interior permanent magnet synchronous motor using direct torque control (DTC). A neural network is used to emulate the state selector of the DTC. The neural networks used in this paper are the back-propagation, radial basis function. In order to reduce the training patterns and increase the execution speed of the training process, the inputs of switching table is converted to digital signals, i.e., one bit represent the flux error, one bit the torque error and three bits the region of stator flux. Computer simulations of the motor and neural-network system using the two approaches are presented and compared. Discussions about the back-propagation and radial basis function as the most promising training techniques are presented, giving its advantages and disadvantages. The system using back-propagation and radial basis function networks controller has quickly parallel speed and high torque response.