Smooth speed control of permanent magnet servo systems using artificial neural network

The project aims to develop a high-performance speed controller for servo systems. In servo applications, the development of high-performance speed controller has been accelerated. The speed controller has to offer high precision, good tracking performance, nice disturbance rejection property, and s...

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Main Author: Wang, Shuoyuan
Other Authors: Christopher H. T. Lee
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/172374
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1723742023-12-08T15:43:28Z Smooth speed control of permanent magnet servo systems using artificial neural network Wang, Shuoyuan Christopher H. T. Lee School of Electrical and Electronic Engineering chtlee@ntu.edu.sg Engineering::Electrical and electronic engineering The project aims to develop a high-performance speed controller for servo systems. In servo applications, the development of high-performance speed controller has been accelerated. The speed controller has to offer high precision, good tracking performance, nice disturbance rejection property, and strong robustness to parameter variations. As a model-free control method, artificial neural network (ANN) based control can enhance the anti-disturbance rejection ability and has a strong robustness to parameter variations. This project allows student to develop an ANN based high-performance speed controller from design, analysis, simulation and evaluation. Exceptional design can be made as experimental prototype for concept verification. Master of Science (Power Engineering) 2023-12-07T06:18:14Z 2023-12-07T06:18:14Z 2023 Thesis-Master by Coursework Wang, S. (2023). Smooth speed control of permanent magnet servo systems using artificial neural network. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/172374 https://hdl.handle.net/10356/172374 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Wang, Shuoyuan
Smooth speed control of permanent magnet servo systems using artificial neural network
description The project aims to develop a high-performance speed controller for servo systems. In servo applications, the development of high-performance speed controller has been accelerated. The speed controller has to offer high precision, good tracking performance, nice disturbance rejection property, and strong robustness to parameter variations. As a model-free control method, artificial neural network (ANN) based control can enhance the anti-disturbance rejection ability and has a strong robustness to parameter variations. This project allows student to develop an ANN based high-performance speed controller from design, analysis, simulation and evaluation. Exceptional design can be made as experimental prototype for concept verification.
author2 Christopher H. T. Lee
author_facet Christopher H. T. Lee
Wang, Shuoyuan
format Thesis-Master by Coursework
author Wang, Shuoyuan
author_sort Wang, Shuoyuan
title Smooth speed control of permanent magnet servo systems using artificial neural network
title_short Smooth speed control of permanent magnet servo systems using artificial neural network
title_full Smooth speed control of permanent magnet servo systems using artificial neural network
title_fullStr Smooth speed control of permanent magnet servo systems using artificial neural network
title_full_unstemmed Smooth speed control of permanent magnet servo systems using artificial neural network
title_sort smooth speed control of permanent magnet servo systems using artificial neural network
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/172374
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