Approximation of Permanent Magnet Motor Flux Distribution by Partially Informed Neural Networks

New results in the area of neural network modeling applied in electric drive automation are presented. Reliable models of permanent magnet motor flux as a function of current and rotor position are particularly useful in control synthesis—allowing one to minimize the losses, analyze motor performance (torque ripples etc.) and to identify motor parameters—and may be used in the control loop to compensate flux and torque variations. The effectiveness of extreme learning machine (ELM) neural networks used for approximation of permanent magnet motor flux distribution is evaluated. Two original network modifications, using preliminary information about the modeled relationship, are introduced. It is demonstrated that the proposed networks preserve all appealing features of a standard ELM (such as the universal approximation property and extremely short learning time), but also decrease the number of parameters and deal with numerical problems typical for ELMs. It is demonstrated that the proposed modified ELMs are suitable for modeling motor flux versus position and current, especially for interior permanent magnet motors. The modeling methodology is presented. It is shown that the proposed approach produces more accurate models and provides greater robustness against learning data noise. The execution times obtained experimentally from well-known DSP boards are short enough to enable application of derived models in modern algorithms of electric drive control.