Nonlinear Modeling, Identification, and Optimal Feedforward Torque Control of Induction Machines Using Steady-State Machine Maps

A novel but simple machine map-based modeling, identification, and optimal feedforward torque control (OFTC) approach for induction machines (IMs) is presented. It is based on, first, a generic, nonlinear transformer-like machine model considering nonlinear flux linkages (with magnetic saturation and cross coupling) and iron losses in the stator laminations in a novel, arbitrarily rotating but unique, robust, and reproducible (
$d,q$
)-reference frame; second, a holistic machine identification procedure, which evaluates steady-state measurements over a grid of (
$d,q$
) stator currents and produces temperature and frequency dependent machine maps, for example, flux linkages, torque, iron resistance, and efficiency; and third, a numerical offline optimization and extraction of different OFTC look-up tables (LUTs) for
optimal
current reference generation depending on reference torque and electrical frequency (and temperature). During the identification, stator winding temperature and electrical stator frequency of the IM are kept constant by an intelligent temperature and the speed control system. The presented measurement results for a squirrel-cage IM confirm that compared to constant flux operation or scalar V/Hz control, efficiency can be increased particularly in part-load operation by up to
$\text{7} \,\%$
by Maximum Torque Per Losses minimizing copper
and
iron losses.