Intelligent active force control of a robotic arm using genetic algorithm

The main requirement of an active force control (AFC) applied to a dynamical system is the estimation of the inertia matrix, IN to compensate for the disturbances and uncertainties in the system. In this paper, genetic algorithm (GA) is used to estimate suitable value of IN of a robotic manipulator...

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Main Authors: Mailah, Musa, Wong, Min Yee, Jamaluddin, Hishamuddin
Format: Article
Language:English
Published: Faculty of Mechanical Engineering 2002
Subjects:
Online Access:http://eprints.utm.my/8286/
http://eprints.utm.my/8286/3/MusaMailah2002_IntelligentActiveForceControlof.pdf
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author Mailah, Musa
Wong, Min Yee
Jamaluddin, Hishamuddin
author_facet Mailah, Musa
Wong, Min Yee
Jamaluddin, Hishamuddin
author_sort Mailah, Musa
building UTeM Institutional Repository
collection Online Access
description The main requirement of an active force control (AFC) applied to a dynamical system is the estimation of the inertia matrix, IN to compensate for the disturbances and uncertainties in the system. In this paper, genetic algorithm (GA) is used to estimate suitable value of IN of a robotic manipulator necessary for the implementation of the AFC strategy through a simulation study. A set of constant torques at the joints is deliberately introduced as the disturbance mechanism to test the effectiveness of the proposed scheme. The results show that the GA used in the study being a stochastic and global optimizer successfully computes appropriate IN value to effect the control action. The proposed scheme exhibits a high degree of robustness and accuracy as the track error is bounded within an acceptable range of value even under the influence of the introduced disturbance.
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spelling utm-82862017-10-16T01:12:19Z http://eprints.utm.my/8286/ Intelligent active force control of a robotic arm using genetic algorithm Mailah, Musa Wong, Min Yee Jamaluddin, Hishamuddin TJ Mechanical engineering and machinery The main requirement of an active force control (AFC) applied to a dynamical system is the estimation of the inertia matrix, IN to compensate for the disturbances and uncertainties in the system. In this paper, genetic algorithm (GA) is used to estimate suitable value of IN of a robotic manipulator necessary for the implementation of the AFC strategy through a simulation study. A set of constant torques at the joints is deliberately introduced as the disturbance mechanism to test the effectiveness of the proposed scheme. The results show that the GA used in the study being a stochastic and global optimizer successfully computes appropriate IN value to effect the control action. The proposed scheme exhibits a high degree of robustness and accuracy as the track error is bounded within an acceptable range of value even under the influence of the introduced disturbance. Faculty of Mechanical Engineering 2002-06 Article PeerReviewed application/pdf en http://eprints.utm.my/8286/3/MusaMailah2002_IntelligentActiveForceControlof.pdf Mailah, Musa and Wong, Min Yee and Jamaluddin, Hishamuddin (2002) Intelligent active force control of a robotic arm using genetic algorithm. Jurnal Mekanikal (13). ISSN 0127-3396 http://jurnalmekanikal.fkm.utm.my/
spellingShingle TJ Mechanical engineering and machinery
Mailah, Musa
Wong, Min Yee
Jamaluddin, Hishamuddin
Intelligent active force control of a robotic arm using genetic algorithm
title Intelligent active force control of a robotic arm using genetic algorithm
title_full Intelligent active force control of a robotic arm using genetic algorithm
title_fullStr Intelligent active force control of a robotic arm using genetic algorithm
title_full_unstemmed Intelligent active force control of a robotic arm using genetic algorithm
title_short Intelligent active force control of a robotic arm using genetic algorithm
title_sort intelligent active force control of a robotic arm using genetic algorithm
topic TJ Mechanical engineering and machinery
url http://eprints.utm.my/8286/
http://eprints.utm.my/8286/
http://eprints.utm.my/8286/3/MusaMailah2002_IntelligentActiveForceControlof.pdf