Learning techniques to train neural networks as a state selector for inverter-fed induction machines using direct torque control

Neural networks are receiving attention as controllers for many industrial applications. Although these networks eliminate the need for mathematical models, they 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 in control of induction machines using direct torque control (DTC). The neural network is used to emulate the state selector of the DTC. The algorithms use to train the neural network are: the back propagation, adaptive neuron model, extended Kalman filter and the parallel recursive prediction error. Computer simulations of the motor and neural network system using the four approaches are presented and compared. The parallel recursive prediction error and the extended Kalman filter algorithms as the most promising training techniques are discussed, giving its advantages over other techniques.<>