DocumentCode :
1460660
Title :
Adaptation learning control scheme for a high-performance permanent-magnet stepper motor using online random training of neural networks
Author :
Rubaai, Ahmed ; Kotaru, Raj
Author_Institution :
Dept. of Electr. Eng., Howard Univ., Washington, DC, USA
Volume :
37
Issue :
2
fYear :
2001
Firstpage :
495
Lastpage :
502
Abstract :
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 identify and adaptively control the speed of the stepper motor is proposed. The control scheme utilizes two three-layer feedforward ANNs: (1) a tracker identification neural network which captures the nonlinear dynamics of the motor over any arbitrary time interval in its range of operation; and (2) a controller neural network to provide the necessary control actions to achieve trajectory tracking of the motor speed. The inputs to the controller neural network are not constructed from the actual motor/load dynamics, but as a feedback signal, from the estimated state variables of the motor supplied by the neural identifier and the reference trajectory to be tracked by the actual speed. A full nonlinear model (with no simplifying assumptions) is used to model the motor dynamics, and to the best of the authors´ knowledge this represents the first such attempt for this device. This paper also makes use of a very realistic and practical scheme to estimate and adaptively learn the noise content in the speed-load torque characteristic of the motor. Simulations reveal that the neural controller adapts and generalizes its learning rate to a wide variety of loads, in addition to providing the necessary abstraction when measurements are contaminated with noise
Keywords :
AC motor drives; adaptive control; angular velocity control; feedback; feedforward neural nets; learning (artificial intelligence); machine control; neurocontrollers; permanent magnet motors; stepping motors; AC motors; ANN; adaptation learning control scheme; adaptive control; arbitrary time interval; artificial neural network control scheme; continual online random training; controller neural network; estimated state variables; feedback signal; high-performance drives environment; high-performance permanent-magnet stepper motor; learning rate; neural networks; nonlinear dynamics; nonlinear model; online random training; speed control; speed-load torque characteristic; three-layer feedforward ANN; tracker identification neural network; Artificial neural networks; Feedforward neural networks; Neural networks; Neurofeedback; Noise measurement; Signal processing; State estimation; State feedback; Torque; Trajectory;
fLanguage :
English
Journal_Title :
Industry Applications, IEEE Transactions on
Publisher :
ieee
ISSN :
0093-9994
Type :
jour
DOI :
10.1109/28.913714
Filename :
913714
Link To Document :
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