Identification and control of induction motor stator currents using fast on-line random training of a neural network
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 μsec. 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.
