Sensorless Direct Torque Control of Induction Motor Using Neural Network-Based Duty Ratio Controller

2017 ◽  
pp. 515-523
Author(s):  
H. Sudheer ◽  
S. F. Kodad ◽  
B. Sarvesh
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Direct Torque Control ◽  
Torque Control ◽  
Duty Ratio

scholarly journals Improved Sensorless Direct Torque Control of Induction Motor Using Fuzzy Logic and Neural Network Based Duty Ratio Controller

2017 ◽  
Vol 6 (2) ◽  
pp. 79
Author(s):  
Sudheer H ◽  
Kodad SF ◽  
Sarvesh B
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Induction Motor ◽  
Direct Torque Control ◽  
Torque Control ◽  
Duty Ratio ◽  
Time Period ◽  
Hysteresis Controller ◽  
Remaining Time ◽  
Selection Of

This paper presents improvements in Direct Torque control of an induction motor using Fuzzy logic with Fuzzy logic and neural network based duty ratio controller. The conventional DTC (CDTC) of induction motor suffers from major drawbacks like high torque and flux ripples and poor transient response. Torque and flux ripples are reduced by replacing hysteresis controller and switching table with Fuzzy logic switching controller (FDTC). In FDTC the selected switching vector is applied for the complete switching time period. The FDTC steady state performance can be improved by using duty ratio controller, the selected switching vector is applied only for the time determined by the duty ratio (δ) and for the remaining time period zero switching vector is applied. The selection of duty ratio using Fuzzy logic and neural networks is projected in this paper. The effectiveness proposed methods are evaluated using simulation by Matlab/Simulink.

scholarly journals Minimization of Torque Ripple in DTC of Induction Motor Using Fuzzy Mode Duty Cycle Controller

2011 ◽  
Vol 7 (1) ◽  
pp. 42-49
Author(s):  
Turki Abdalla ◽  
Haroution Hairik ◽  
Adel Dakhil
Keyword(s):  
Induction Motor ◽  
Duty Cycle ◽  
Direct Torque Control ◽  
Torque Ripple ◽  
Operating Conditions ◽  
Torque Control ◽  
Voltage Source ◽  
Ratio Method ◽  
Duty Ratio ◽  
Switching Frequency

Among all control methods for induction motor drives, Direct Torque Control (DTC) seems to be particularly interesting being independent of machine rotor parameters and requiring no speed or position sensors. The DTC scheme is characterized by the absence of PI regulators, coordinate transformations, current regulators and PWM signals generators. In spite of its simplicity, DTC allows a good torque control in steady state and transient operating conditions to be obtained. However, the presence of hysterics controllers for flux and torque could determine torque and current ripple and variable switching frequency operation for the voltage source inverter. This paper is aimed to analyze DTC principles, and the problems related to its implementation, especially the torque ripple and the possible improvements to reduce this torque ripple by using a proposed fuzzy based duty cycle controller. The effectiveness of the duty ratio method was verified by simulation using Matlab/Simulink software package. The results are compared with that of the traditional DTC models.

scholarly journals Online Speed Estimation Using Artificial Neural Network for Speed Sensorless Direct Torque Control of Induction Motor based on Constant V/F Control Technique

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2176 ◽  
Author(s):  
Narongrit Pimkumwong ◽  
Ming-Shyan Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Direct Torque Control ◽  
Torque Control ◽  
Control Technique ◽  
Speed Estimation ◽  
Speed Sensorless ◽  
Stator Current ◽  
Artificial Neural

This paper presents the speed estimator for speed sensorless direct torque control of a three-phase induction motor based on constant voltage per frequency (V/F) control technique, using artificial neural network (ANN). The estimated stator current equation is derived and rearranged consistent with the control algorithm and ANN structure. For the speed estimation, a weight in ANN, which relates to the speed, is adjusted by using Widrow–Hoff learning rule to minimize the sum of squared errors between the measured stator current and the estimated stator current from ANN output. The consequence of using this method leads to the ability of online speed estimation and simple ANN structure. The simulation and experimental results in high- and low-speed regions have confirmed the validity of the proposed speed estimation method.

scholarly journals High-performance adaptive intelligent Direct Torque Control schemes for induction motor drives

2005 ◽  
Vol 2 (1) ◽  
pp. 93-116 ◽  
Author(s):  
M. Vasudevan ◽  
R. Arumugam ◽  
S. Paramasivam
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Induction Motor ◽  
High Performance ◽  
Fuzzy Controller ◽  
Linear Time ◽  
Direct Torque Control ◽  
Torque Control ◽  
Advantages And Disadvantages ◽  
State Selection

This paper presents a detailed comparison between viable adaptive intelligent torque control strategies of induction motor, emphasizing advantages and disadvantages. The scope of this paper is to choose an adaptive intelligent controller for induction motor drive proposed for high performance applications. Induction motors are characterized by complex, highly non-linear, time varying dynamics, inaccessibility of some states and output for measurements and hence can be considered as a challenging engineering problem. The advent of torque and flux control techniques have partially solved induction motor control problems, because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers are used. Intelligent controllers are considered as potential candidates for such an application. In this paper, the performance of the various sensor less intelligent Direct Torque Control (DTC) techniques of Induction motor such as neural network, fuzzy and genetic algorithm based torque controllers are evaluated. Adaptive intelligent techniques are applied to achieve high performance decoupled flux and torque control. This paper contributes: i) Development of Neural network algorithm for state selection in DTC; ii) Development of new algorithm for state selection using Genetic algorithm principle; and iii) Development of Fuzzy based DTC. Simulations have been performed using the trained state selector neural network instead of conventional DTC and Fuzzy controller instead of conventional DTC controller. The results show agreement with those of the conventional DTC.

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