NDLI: Improvement of an induction motor drive-based direct torque control strategy using a neuro-fuzzy controller

Content Provider	IEEE Xplore Digital Library
Author	Gama Valdez, M.A. Romero Romero, D.
Copyright Year	2004
Abstract	This work describes how a Neuro-Fuzzy network (ANFIS, Adaptive Network-based Fuzzy Inrerence System) is used to control an Induction Motor Drive-based Direct Torque Control strateLv to improve the dynamic behavior or the system and simplify the conventional direct torque control scheme. The Neuro-Fuzzy controller¿s structure guides the torque and stator flux error signals through the fuzzy inference to get an output that takes the form of a space voltage vector to be modulated by the inverter. Besides this intelligent controller, Space Vector Modulation is applied to avoid hysteresis bands and switching tables which are common in the conventional scheme. Under this frame it is possible to set a constant value for the switching frequency and, in general, improve the dynamic behavior of the system. Simulations are presented to show that the proposed Intelligent System is characterized not only by simple mathematical models, but also by very fast torque and flux responses. Furthermore, zero-steady-state error in torque and flux is gotten thanks to inverse learning and the tuning algorithm appiied to the network. This control also provides a natural and well-defined human-machine interaction.
Starting Page	439
Ending Page	444
File Size	1004131
Page Count	6
File Format	PDF
ISBN	0780385314
DOI	10.1109/ICEEE.2004.1433922
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-08
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pulse width modulation inverters Induction motor drives Space vector pulse width modulation Fuzzy control Induction motors Torque control Control systems Fuzzy neural networks Hysteresis Fuzzy systems
Content Type	Text
Resource Type	Article

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