Added Title
OPTIMIZATION OF AN ANN-BASED SPEED AND POSITION ESTIMATOR FOR AN FOC-CONTROLLED PMSM USING GENETIC ALGORITHM
Date of Publication
9-12-2022
Document Type
Master's Thesis
Degree Name
Master of Science in Electronics and Communications Engineering
Subject Categories
Computational Engineering | Controls and Control Theory | Electrical and Electronics | Signal Processing
College
Gokongwei College of Engineering
Department/Unit
Electronics And Communications Engg
Thesis Advisor
Edwin Sybingco
Defense Panel Chair
Maria Antonette Roque
Defense Panel Member
Alvin Chua
Leonard Ambata
Abstract/Summary
This study develops a neural network-based estimator for the speed and position of a field-oriented-controlled permanent magnet synchronous motor optimized using a genetic algorithm. An estimator based on a neural network provides an alternative to conventional methods that require accurate information on the motor parameters. Genetic Algorithm provides an avenue to optimize the hyperparameters for optimal performance. A training dataset is obtained from the motor operating points consisting of the alpha- beta voltages and currents with the sin and cosine of the rotor position as the targets. A genetic algorithm was used to determine the optimal hyperparameters for the network’s batch size, the training algorithm parameters, and the number of hidden layers and its respective number of neurons. In this study, the genetic algorithm developed was able to optimize the hyperparameters for the neural network to achieve a high accuracy over the operating range. The neural network-based estimator can estimate the speed and position of the PMSM required in executing the field-oriented control scheme. The optimized neural network proved to have more accurate estimations than conventional methods such as the SMO and MRAS as well as other neural network estimators during steady-state and dynamic conditions, including when qualified using a UAV Flight Plan. The efficiency of the proposed estimator proved to be relatively higher than the conventional estimators but still fall short of the efficiency when using sensors.
Abstract Format
html
Language
English
Format
Electronic
Electronic File Format
MS WORD
Keywords
genetic algorithm, speed and position estimator, field-oriented control, permanent magnet synchronous motor, optimization
Recommended Citation
Quismundo, J. B. (2022). OPTIMIZATION OF AN ANN-BASED SPEED AND POSITION ESTIMATOR FOR AN FOC-CONTROLLED PMSM USING GENETIC ALGORITHM. Retrieved from https://animorepository.dlsu.edu.ph/etdm_ece/17
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Chapter 1
2022_Quismundo_Chapter2.pdf (168 kB)
Chapter 2
2022_Quismundo_Chapter3.pdf (629 kB)
Chapter 3
2022_Quismundo_Chapter4.pdf (675 kB)
Chapter 4
2022_Quismundo_Chapter5.pdf (1745 kB)
Chapter 5
2022_Quismundo_Chapter6.pdf (41 kB)
Chapter 6
2022_Quismundo_AppendixA.pdf (94 kB)
Appendix A
2022_Quismundo_AppendixB.pdf (91 kB)
Appendix B
2022_Quismundo_ApprovalSheet.pdf (444 kB)
Approval Sheet
2022_Quismundo_References.pdf (143 kB)
References
Embargo Period
9-9-2032
