Neural network self-tuning PID based navigation control of autonomous unicycle-like mobile robot in industry 4.0

In the recent years, mobile robots, one of the technologies under the “Industry 4.0” concept, have been used in a wide range of industry sectors, including manufacturing and production, agriculture, healthcare, etc. One of the applications of a mobile robot is transportation to deliver things from o...

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Main Author:	Cheok, Jun Yi
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/99370/1/CheokJunYiMSKE2022.pdf
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id	my-utm-ep.99370
record_format	uketd_dc
spelling	my-utm-ep.993702023-02-23T04:07:53Z Neural network self-tuning PID based navigation control of autonomous unicycle-like mobile robot in industry 4.0 2022 Cheok, Jun Yi TK Electrical engineering. Electronics Nuclear engineering In the recent years, mobile robots, one of the technologies under the “Industry 4.0” concept, have been used in a wide range of industry sectors, including manufacturing and production, agriculture, healthcare, etc. One of the applications of a mobile robot is transportation to deliver things from one place to another, following the planned trajectory. The conventional way of controlling the trajectory tracking of a mobile robot is by using the classical PID control schemes. However, it has been found that the performance was not very satisfying because PID controllers have a weak adaptability to the mobile robot dynamic system which consists of nonlinearity and uncertainty that varies with time. In order to achieve adaptive controller, this study proposes a Neural Network (NN) self-tuning PID based navigation control which is capable to perform on-line tuning of the PID parameters to meet the desired control performance and stability during operation. In this work, MATLAB-Simulink software is used to simulate the dynamic model of a unicycle-like mobile robot. PID controllers which are tuned with the Trial & Error method is firstly used to control the trajectory tracking of the mobile robot. Then, the same dynamic model is controlled by using the proposed NN self-tuning PID controllers. The simulation results obtained from both simulations are compared from the aspect of the distance error and energy consumption by calculating the IAE index and kinetic energy index, and the results show the capability of the NN self-tuning PID controllers to perform better than a PID controller in a non-linear system. 2022 Thesis http://eprints.utm.my/id/eprint/99370/ http://eprints.utm.my/id/eprint/99370/1/CheokJunYiMSKE2022.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149987 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Cheok, Jun Yi Neural network self-tuning PID based navigation control of autonomous unicycle-like mobile robot in industry 4.0
description	In the recent years, mobile robots, one of the technologies under the “Industry 4.0” concept, have been used in a wide range of industry sectors, including manufacturing and production, agriculture, healthcare, etc. One of the applications of a mobile robot is transportation to deliver things from one place to another, following the planned trajectory. The conventional way of controlling the trajectory tracking of a mobile robot is by using the classical PID control schemes. However, it has been found that the performance was not very satisfying because PID controllers have a weak adaptability to the mobile robot dynamic system which consists of nonlinearity and uncertainty that varies with time. In order to achieve adaptive controller, this study proposes a Neural Network (NN) self-tuning PID based navigation control which is capable to perform on-line tuning of the PID parameters to meet the desired control performance and stability during operation. In this work, MATLAB-Simulink software is used to simulate the dynamic model of a unicycle-like mobile robot. PID controllers which are tuned with the Trial & Error method is firstly used to control the trajectory tracking of the mobile robot. Then, the same dynamic model is controlled by using the proposed NN self-tuning PID controllers. The simulation results obtained from both simulations are compared from the aspect of the distance error and energy consumption by calculating the IAE index and kinetic energy index, and the results show the capability of the NN self-tuning PID controllers to perform better than a PID controller in a non-linear system.
format	Thesis
qualification_level	Master's degree
author	Cheok, Jun Yi
author_facet	Cheok, Jun Yi
author_sort	Cheok, Jun Yi
title	Neural network self-tuning PID based navigation control of autonomous unicycle-like mobile robot in industry 4.0
title_short	Neural network self-tuning PID based navigation control of autonomous unicycle-like mobile robot in industry 4.0
title_full	Neural network self-tuning PID based navigation control of autonomous unicycle-like mobile robot in industry 4.0
title_fullStr	Neural network self-tuning PID based navigation control of autonomous unicycle-like mobile robot in industry 4.0
title_full_unstemmed	Neural network self-tuning PID based navigation control of autonomous unicycle-like mobile robot in industry 4.0
title_sort	neural network self-tuning pid based navigation control of autonomous unicycle-like mobile robot in industry 4.0
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2022
url	http://eprints.utm.my/id/eprint/99370/1/CheokJunYiMSKE2022.pdf
_version_	1776100593890230272

Neural network self-tuning PID based navigation control of autonomous unicycle-like mobile robot in industry 4.0

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