Predictive torque and flux control of an induction machine drive using fuzzy multi-criteria decision making

Among the numerous direct torque control techniques, the finite-state predictive torque control (FS-PTC) has emerged as a powerful alternative as it offers the fast dynamic response and the flexibility to optimize multiple objectives simultaneously. However, the implementation of FS-PTC for multiple objectives optimization requires the optimization of a single objective function, which is constructed using weighting factors as a linear combination of individual objective functions. Traditionally, the weighting factors are determined through a non-trivial process, which is a complex and time-consuming task. In an effort to avoid the time-consuming task of weighting factor selection, this paper aims at replacing the weighting factor calculation with a systematic fuzzy multiple-criteria decision making in which the individual objective functions may have equal or varying degrees of importance. As a result the weighting factor calculation can be completely avoided. The simulation and experimental tests are conducted on a 2.2 kW induction motor drive to validate the proposed approach. The result outcomes are compared with the conventional predictive torque control (PTC) using weighting factors on the same experimental platform.