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New. Power Systems . XXVI, 222 p. Intended for college/higher education audience. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
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Fine. Power Systems . XXVI, 222 p. Intended for college/higher education audience. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
Vector Control of Induction Machines: Desensitisation and Optimisation Through Fuzzy Logic (Power Systems) [Hardcover] Robyns, Benoît; Francois, Bruno; Degobert, Philippe and Hautier, Jean Paul
Vector Control of Induction Machines: Desensitisation and Optimisation Through Fuzzy Logic (Power Systems) [Englisch] [Gebundene Ausgabe] Benoît Robyns (Autor), Bruno Francois (Autor), Philippe Degobert (Autor), Jean Paul Hautier (Autor)
by Benoît Robyns (Autor), Bruno Francois (Autor), Philippe Degobert (Autor), Jean Paul Hautier (Autor)
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Very good in very good dust jacket. After a brief introduction to the main law of physics and fundamental concepts inherent in electromechanical conversion, Vector Control of Induction Machines introduces the standard mathematical models for induction machines – whichever rotor technology is used – as well as several squirrel-cage induction machine vector-control strategies. The use of causal ordering graphs allows systematization of the design stage, as well as standardization of the structure of control devices. Vector Control of Induction Machines suggests a unique approach aimed at reducing parameter sensitivity for vector controls based on a theoretical analysis of this sensitivity. This analysis naturally leads to the introduction of control strategies that are based on the combination of different controls with different robustness properties, through the use of fuzzy logic supervisors. Numerous applications and experiments confirm the validity of this simple solution, which is both reproducible and applicable to other complex systems.
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