Adaptive Nonlinear Direct Torque Control of Sensorless IM Drives With Efficiency Optimization
Efficiency optimization of induction motor (IM) drives is a major subject based on these drives' extensive use in the industry. Among the different proposed methods, a model-based approach (MBA) seems to be the fast one. However, this method needs the motor parameters that must be correctly ide...
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Published in | IEEE transactions on industrial electronics (1982) Vol. 57; no. 3; pp. 975 - 985 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.03.2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Efficiency optimization of induction motor (IM) drives is a major subject based on these drives' extensive use in the industry. Among the different proposed methods, a model-based approach (MBA) seems to be the fast one. However, this method needs the motor parameters that must be correctly identified. On the other hand, a search-based approach (SBA) is a parameter-independent method but needs a greater convergence time. In this paper, a novel model-based loss-minimization approach is presented, which is combined with a backstepping direct torque control of the IM drive. An improved search-based method for efficiency optimization is also introduced. The proposed controller is realized in the stationary reference frame and has a fast-tracking capability of rotor flux and electromagnetic torque. Moreover, a sliding-mode rotor-flux observer is introduced, which is employed for simultaneous determination of rotor-flux space vector, rotor speed, and rotor time constant. The proposed control idea is experimentally implemented in real time using a field-programmable gate-array board synchronized with a personal computer. Simulation and experimental results are finally presented to verify the effectiveness of the method proposed. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 |
ISSN: | 0278-0046 1557-9948 |
DOI: | 10.1109/TIE.2009.2029592 |