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

• Published in: IEEE transactions on power electronics Vol. 12; no. 5; pp. 788 - 799
• Main Authors: Cabrera, L.A., Elbuluk, M.E., Zinger, D.S.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.1997; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Application software; Applied sciences; Backpropagation algorithms; Computer errors; Convergence; Electrical engineering. Electrical power engineering; Electrical machines; Electronics; Exact sciences and technology; Industrial control; Industrial training; Machinery; Mathematical model; Neural networks; Regulation and control; Stability; Torque control; Parallel algorithm; Adaptive algorithm; Electric drive; Neural network; Recursive algorithm; Backpropagation algorithm; Ac motor; Torque control; Induction machine; Descent method; Kalman filtering; Newton method; Gradient method
• ISSN: 0885-8993; 1941-0107
• DOI: 10.1109/63.622996

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 to control induction machines using direct torque control (DTC). A neural network is used to emulate the state selector of the DTC. The training algorithms used in this paper are the backpropagation, 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. Discussions about the parallel recursive prediction error and the extended Kalman filter algorithms as the most promising training techniques is presented, giving their advantages and disadvantages.