Grey Relational Analysis-Based Objective Function Optimization for Predictive Torque Control of Induction Machine

This article presents grey relational analysis (GRA)-based objective function optimization in predictive torque control (PTC) for induction machine. Selection of appropriate weighting factor in the objective function is one of the key aspects in the implementation of PTC. However, selection of suita...

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Bibliographic Details
Published in:IEEE transactions on industry applications Vol. 57; no. 1; pp. 835 - 844
Main Authors: Muddineni, Vishnu Prasad, Bonala, Anil Kumar, Sandepudi, Srinivasa Rao
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Control algorithms
Control theory
Estimation
Grey relational analysis
induction machines
Induction motors
Linear programming
objective function optimization
Optimization
Predictive control
predictive torque control
Rotors
Stators
Switches
Torque
Weighting
weighting factor
ISSN:0093-9994, 1939-9367
Online Access:Get full text
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Summary:This article presents grey relational analysis (GRA)-based objective function optimization in predictive torque control (PTC) for induction machine. Selection of appropriate weighting factor in the objective function is one of the key aspects in the implementation of PTC. However, selection of suitable weighting factor in the objective function is a heuristic task. To address this issue, GRA method is implemented for the objective function optimization. In this approach, single-objective function is modified into two independent objective functions for stator flux and torque. A grey relational grade is used to identify the suitable control action in each sampling. A MATLAB/Simulink model is developed to validate the control algorithm under various operating conditions of the drive, and corresponding results are compared with experimental results.
Bibliography:ObjectType-Article-1
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ObjectType-Feature-2
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ISSN:0093-9994
1939-9367
DOI:10.1109/TIA.2020.3037875