Adaptation learning control scheme for a high-performance permanent-magnet stepper motor using online random training of neural networks

• Published in: IEEE transactions on industry applications Vol. 37; no. 2; pp. 495 - 502
• Main Authors: Rubaai, A., Kotaru, R.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2001; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Computer simulation; Feedforward neural networks; Mathematical models; Motors; Neural networks; Neurofeedback; Noise measurement; Nonlinear dynamics; Signal processing; State estimation; State feedback; Steppers; Studies; Torque; Training; Trajectories; Trajectory

This paper addresses the problem of controlling the speed of a permanent-magnet stepper motor assumed to operate in a high-performance drives environment. An artificial neural network (ANN) control scheme which uses continual online random training (with no offline training) to simultaneously identi...