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Intelligent Control of Induction Motor

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The Direct Torque Control (DTC) is one of the most effective control and modern methods for high performance AC drives in a wide variety of industrial application and conventional DTC technique uses two constant reference values stator flux and torque ripple. In this paper, two approach intelligent techniques such as fuzzy logic (FL) and Artificial Neural Networks (ANN) used to improving the performance of DTC drive in terms of switching frequency and stator flux estimation. In this work a proposed DTC system supported by a Neuro –Fuzzy Controller are constructed to avoid the inherent torque ripples. Simulation studies have been carried out with using MATLAB program. The proposed Direct Torque Control (DTC) system shows a considerable reduction in torque ripples and best starting performance. This improvement leads to an ability to increase the performance at low-load condition torque ripple are greatly reduced with respect to the conventional DTC.
Keywords:Direct Torque Control (DTC), Field Oriented Control (FOC), Fuzzy Logic (FL), Fuzzy Inference System (FIS) and Artificial Neural Network (ANN).
Nowadays, Asynchronous motor drives with cage type machines have been the work horses in industry for variable speed application in a wide power range that covers from fractional horse power to multi-megawatts. These application include pumps, Fans, Paper textile mills, Subway and locomotive propulsions, electric and hybrid vehicles machine tools & robotics, home appliances, heat pumps, air conditioners, rolling mills, wind generation pumped storage system, etc.[1] The control and estimation of asynchronous drives in general is considerably more complex than those of dc drives, and this complexity increases substantially if high performances are demanded. The different control techniques of induction motor drives, including Scalar control, Vector or Field Oriented Control (FOC), Direct Torque and Flux Control (DTC (or) DTFC) or Direct Self Control (DSC) and Adaptive control. Scalar control is based on the steady state motor model while Vector control is based on dynamic model of motor. [2]. Scalar control, as the name indicates is due to magnitude variation of the control variables only, and disregards the coupling effect in the machine. For example, the voltage of a machine can be controlled to control the flux, and frequency or slip can be controlled to control the torque. Scalar controls are easy to implement and have been widely used in industry. Scalar control techniques with voltage fed and current –fed inverters etc.,
The invention of Vector control or Field Oriented Control (FOC) in the beginning of 1970s, FOC was first introduced by Blaschke [3]. In vector control and the corresponding feedback signal processing, particularly for modern sensor less vector control are complex and the use of powerful microcomputer or DSP is mandatory. In this method to provide satisfactory steady-state performance, but their dynamic response is poor. In addition to this some drawbacks are separate current loops; Pulse Width Modulation (PWM) and Co-ordinate transformation are required. In the mid-1980s, an advanced Scalar Control technique, known as Direct Torque and Flux Control (DTFC or DTC) or Direct Self-control (DSC) was introduced for voltage-fed PWM inverter drives.
The DTC proposed by Takashi and M. Depenbrock [4] for variable load and speed asynchronous motor drives. It was a good alternative to the other type of vector control which known as FOC due to some well-known advantages, such as simple control structure, robust and fast torque response without co-ordinate transformation PWM pulse generation and current regulators moreover, DTC minimizes the use of motor parameters. Besides these advantages, DTC scheme still had some disadvantages like high torque and current ripples, possible problems during starting and low speed operation, variable switching frequency.


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