Artificial neural network based optimal feedforward torque control of
electrically excited synchronous machines
Abstract
An Artificial Neural Network (ANN) based Optimal Feedforward Torque
Control (OFTC) strategy for electrically excited synchronous machines
(EESMs) is proposed. After design, data set creation, training and
validation of the ANN, the analytical computation of the optimal stator
and exciter currents is achieved which allows to minimize copper and
iron losses and to produce the desired (or maximally feasible) machine
torque. Voltage and currents constraints of stator and exciter are
considered as well. In contrast to conventional OFTC, the proposed
ANN-based OFTC strategy does not require iterations nor a decision tree
to find the optimal current triple while machine nonlinearities,
magnetic cross-coupling, saturation and speed-dependent iron losses are
taken into account. In addition, the proposed ANN design procedure
allows to consider measurable OFTC goals and computational
resources that ensures a real-time capable implementation. Comprehensive
simulation results for a real and nonlinear EESM clearly show these
benefits by comparing the proposed ANN-based OFTC with results of a
nonlinear optimization problem (NLP) solver (which cannot be used in
real-time).