Intelligent Optimization of Novel Particle Swarm Optimization with Explorer (PSOE) for Identification of Flexible Manipulator System

Flexible manipulator is widely used in robotics and mechanical systems. Its application have led to the development of systems which are lighter, less bulky, and provides greater system flexibility. However, the flexible manipulator has one drawback. It develops unwanted vibration during operation w...

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Published in:Lecture Notes in Electrical Engineering
Main Author: Yatim H.M.; Zamri A.N.Y.; Hadi M.S.; Talib M.H.A.; Darus I.Z.M.
Format: Conference paper
Language:English
Published: Springer Science and Business Media Deutschland GmbH 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131121219&doi=10.1007%2f978-981-19-2095-0_31&partnerID=40&md5=4cd5afcd3c49fe350ad0879434c94c38
id 2-s2.0-85131121219
spelling 2-s2.0-85131121219
Yatim H.M.; Zamri A.N.Y.; Hadi M.S.; Talib M.H.A.; Darus I.Z.M.
Intelligent Optimization of Novel Particle Swarm Optimization with Explorer (PSOE) for Identification of Flexible Manipulator System
2022
Lecture Notes in Electrical Engineering
900

10.1007/978-981-19-2095-0_31
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131121219&doi=10.1007%2f978-981-19-2095-0_31&partnerID=40&md5=4cd5afcd3c49fe350ad0879434c94c38
Flexible manipulator is widely used in robotics and mechanical systems. Its application have led to the development of systems which are lighter, less bulky, and provides greater system flexibility. However, the flexible manipulator has one drawback. It develops unwanted vibration during operation which reduced the efficiency of the flexible manipulator systems for accurate positioning requirements. Therefore, an intelligent optimizer, the Particle Swarm Optimization with Explorer (PSOE) was developed to model this highly non-linear and complex system. Initially, an experimental setup for the flexible manipulator was developed. Experimental input output data were acquired including hub angle and endpoint acceleration to fed into system identification method. Next, optimization was done using the proposed PSOE as compared to a standard Particle Swarm Optimization (PSO) algorithm via linear auto regressive with exogenous (ARX) model structure. Validations of the algorithms were attained on the basis of minimizing the value of mean-squared error (MSE) and correlation tests. The superiority of the added ‘explorer’ to the algorithm was confirmed as PSOE obtained the lowest MSE value of 2.8232 × 10–5 and 3.7364 × 10–7 for end-point acceleration and hub angle modelling, respectively. Both modelling also achieved good correlation values within the 95% confidence interval. Results obtained can be adapted for further analysis in implementing an active vibration control for flexible manipulator systems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Springer Science and Business Media Deutschland GmbH
18761100
English
Conference paper

author Yatim H.M.; Zamri A.N.Y.; Hadi M.S.; Talib M.H.A.; Darus I.Z.M.
spellingShingle Yatim H.M.; Zamri A.N.Y.; Hadi M.S.; Talib M.H.A.; Darus I.Z.M.
Intelligent Optimization of Novel Particle Swarm Optimization with Explorer (PSOE) for Identification of Flexible Manipulator System
author_facet Yatim H.M.; Zamri A.N.Y.; Hadi M.S.; Talib M.H.A.; Darus I.Z.M.
author_sort Yatim H.M.; Zamri A.N.Y.; Hadi M.S.; Talib M.H.A.; Darus I.Z.M.
title Intelligent Optimization of Novel Particle Swarm Optimization with Explorer (PSOE) for Identification of Flexible Manipulator System
title_short Intelligent Optimization of Novel Particle Swarm Optimization with Explorer (PSOE) for Identification of Flexible Manipulator System
title_full Intelligent Optimization of Novel Particle Swarm Optimization with Explorer (PSOE) for Identification of Flexible Manipulator System
title_fullStr Intelligent Optimization of Novel Particle Swarm Optimization with Explorer (PSOE) for Identification of Flexible Manipulator System
title_full_unstemmed Intelligent Optimization of Novel Particle Swarm Optimization with Explorer (PSOE) for Identification of Flexible Manipulator System
title_sort Intelligent Optimization of Novel Particle Swarm Optimization with Explorer (PSOE) for Identification of Flexible Manipulator System
publishDate 2022
container_title Lecture Notes in Electrical Engineering
container_volume 900
container_issue
doi_str_mv 10.1007/978-981-19-2095-0_31
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131121219&doi=10.1007%2f978-981-19-2095-0_31&partnerID=40&md5=4cd5afcd3c49fe350ad0879434c94c38
description Flexible manipulator is widely used in robotics and mechanical systems. Its application have led to the development of systems which are lighter, less bulky, and provides greater system flexibility. However, the flexible manipulator has one drawback. It develops unwanted vibration during operation which reduced the efficiency of the flexible manipulator systems for accurate positioning requirements. Therefore, an intelligent optimizer, the Particle Swarm Optimization with Explorer (PSOE) was developed to model this highly non-linear and complex system. Initially, an experimental setup for the flexible manipulator was developed. Experimental input output data were acquired including hub angle and endpoint acceleration to fed into system identification method. Next, optimization was done using the proposed PSOE as compared to a standard Particle Swarm Optimization (PSO) algorithm via linear auto regressive with exogenous (ARX) model structure. Validations of the algorithms were attained on the basis of minimizing the value of mean-squared error (MSE) and correlation tests. The superiority of the added ‘explorer’ to the algorithm was confirmed as PSOE obtained the lowest MSE value of 2.8232 × 10–5 and 3.7364 × 10–7 for end-point acceleration and hub angle modelling, respectively. Both modelling also achieved good correlation values within the 95% confidence interval. Results obtained can be adapted for further analysis in implementing an active vibration control for flexible manipulator systems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
publisher Springer Science and Business Media Deutschland GmbH
issn 18761100
language English
format Conference paper
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record_format scopus
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