Abstract
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.
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Appendix
Appendix
Induction machine parameters used for simulation and experimental tests
Parameter | Value |
|---|---|
R s | 5.46 Ω |
L s | 0.3643 H |
R r | 2.68 Ω |
L r | 0.3643 H |
L m | 0.34 H |
ω nom | 1410 RPM |
T nom | 14 Nm |
P nom | 2.2 kW |
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Kumar, V., Gaur, P. & Mittal, A.P. Predictive torque and flux control of an induction machine drive using fuzzy multi-criteria decision making. Sādhanā 42, 343–352 (2017). https://doi.org/10.1007/s12046-017-0606-z
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DOI: https://doi.org/10.1007/s12046-017-0606-z