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 pretraining, 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 form of the random weight change (RWC) algorithm, which is based on the method of random search for the error surface gradient. Simulation results show that the new form of the RWC, termed continually online trained RWC (COT-RWC), yields performance very much the same as conventional backpropagation with on-line training. Unlike backpropagation, however, the COT-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 completes one training cycle in as little as 8 /spl mu/s. 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.