IDENTIFICATION AND CONTROL OF INDUCTION MOTOR STATOR NETWORK CURRENTS USING FAST ON-LINE RANDOM TRAINING OF A NEURAL

Artificial Neural Networks (ANNs) which have no off-line pre-training, can be trained continually on-line to identify an inverter fed induction motor and control its stator currents. Due to the small time constants of the motor circuits, the time to complete one training cycle has to be extremely small. This paper proposes and evaluates a new, fast, on-line training algorithm which is based on the method of random search training, termed the Random Weight Change (RWC) algorithm. Simulation results show that RWC training of an ANN yields performance very much the same as conventional backpropagation training. Unlike backpropagation, however, the RWC method can be implemented in mixed digital/analog hardware, and still have a sufficiently small training cycle time.. The paper also proposes a VLSI implementation which one training cycle in as little as 8 psec. Such a fast ANN can identify and control the motor currents within a few milliseconds and thus provide self-tuning of the drive while the ANN has no prior information whatsoever of the connected inverter and motor.

[1]  B. Burton,et al.  Reducing the computational demands of continually online trained artificial neural networks for system identification and control of fast processes , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[2]  Clark S. Lindsey,et al.  An implementation of the Zer o Instruction Set Computer (ZISC036) on a PC/ISA-bus card , 1994 .

[3]  Ronald G. Harley,et al.  Implementation of a neural network to adaptively identify and control VSI fed induction motor stator currents , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[4]  Chin Park,et al.  A radial basis function neural network with on-chip learning , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[5]  Ronald G. Harley,et al.  Identification and control of induction machines using artificial neural networks , 1993 .

[6]  K. Hirotsu,et al.  An analog neural network chip with random weight change learning algorithm , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[7]  R.D. Lorenz,et al.  Design and implementation of neural networks for digital current regulation of inverter drives , 1991, Conference Record of the 1991 IEEE Industry Applications Society Annual Meeting.

[8]  David J. Atkinson,et al.  Observers for induction motor state and parameter estimation , 1991 .