Hybrid intelligent active force controller for robot arms using evolutionary neural networks

In this paper, we propose a hybrid intelligent parameter estimator for the active force control (AFC) scheme which utilizes evolutionary computation (EC) and artificial neural networks (ANN) to control a rigid robot arm. The EC part of the algorithm composes of a hybrid genetic algorithm (GA) and an...

Full description

Saved in:
Bibliographic Details
Main Authors: Hussein, S. B., Jamaluddin, H., Mailah, M., Zalzala, A. M. S.
Format: Article
Published: IEEE, Piscataway, NJ, United States 2000
Subjects:
Online Access:http://eprints.utm.my/id/eprint/7086/
http://ieeexplore.ieee.org/document/870284/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.7086
record_format eprints
spelling my.utm.70862017-10-22T08:11:10Z http://eprints.utm.my/id/eprint/7086/ Hybrid intelligent active force controller for robot arms using evolutionary neural networks Hussein, S. B. Jamaluddin, H. Mailah, M. Zalzala, A. M. S. TJ Mechanical engineering and machinery In this paper, we propose a hybrid intelligent parameter estimator for the active force control (AFC) scheme which utilizes evolutionary computation (EC) and artificial neural networks (ANN) to control a rigid robot arm. The EC part of the algorithm composes of a hybrid genetic algorithm (GA) and an evolutionary program (EP). The development of the controller is divided into two stages. In the first stage, which is performed off-line, the proposed EC algorithm is employed to evolve a pool of ANN structures until they converge to an optimum structure. The population is divided into different groups according to their fitness. The elitist group will not undergo any operation, while the second group, i.e. stronger group, undergoes the EP operation. Hence, the behavioral link between the parent and their offspring can be maintained. The weaker group undergoes a GA operation since their behaviors need to be changed more effectively in order to produce better offspring. In the second stage, the evolved ANN obtained from the first stage, which represent the optimum ANN structural design, is used to design the on-line intelligent parameter estimator to estimate the inertia matrix of the robot arm for the AFC controller. In this on-line stage, the ANN parameters, i.e. the weights and biases, are further trained using live data and back-propagation until a satisfactory result is obtained. The effectiveness of the proposed scheme is demonstrated through a simulation study performed on a two link planar manipulator operating in a horizontal plane. An external load is introduced to the manipulator to study the effectiveness of the proposed scheme. IEEE, Piscataway, NJ, United States 2000 Article PeerReviewed Hussein, S. B. and Jamaluddin, H. and Mailah, M. and Zalzala, A. M. S. (2000) Hybrid intelligent active force controller for robot arms using evolutionary neural networks. Proceedings of the IEEE Conference on Evolutionary Computation, ICEC, 1 . pp. 117-124. http://ieeexplore.ieee.org/document/870284/
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Hussein, S. B.
Jamaluddin, H.
Mailah, M.
Zalzala, A. M. S.
Hybrid intelligent active force controller for robot arms using evolutionary neural networks
description In this paper, we propose a hybrid intelligent parameter estimator for the active force control (AFC) scheme which utilizes evolutionary computation (EC) and artificial neural networks (ANN) to control a rigid robot arm. The EC part of the algorithm composes of a hybrid genetic algorithm (GA) and an evolutionary program (EP). The development of the controller is divided into two stages. In the first stage, which is performed off-line, the proposed EC algorithm is employed to evolve a pool of ANN structures until they converge to an optimum structure. The population is divided into different groups according to their fitness. The elitist group will not undergo any operation, while the second group, i.e. stronger group, undergoes the EP operation. Hence, the behavioral link between the parent and their offspring can be maintained. The weaker group undergoes a GA operation since their behaviors need to be changed more effectively in order to produce better offspring. In the second stage, the evolved ANN obtained from the first stage, which represent the optimum ANN structural design, is used to design the on-line intelligent parameter estimator to estimate the inertia matrix of the robot arm for the AFC controller. In this on-line stage, the ANN parameters, i.e. the weights and biases, are further trained using live data and back-propagation until a satisfactory result is obtained. The effectiveness of the proposed scheme is demonstrated through a simulation study performed on a two link planar manipulator operating in a horizontal plane. An external load is introduced to the manipulator to study the effectiveness of the proposed scheme.
format Article
author Hussein, S. B.
Jamaluddin, H.
Mailah, M.
Zalzala, A. M. S.
author_facet Hussein, S. B.
Jamaluddin, H.
Mailah, M.
Zalzala, A. M. S.
author_sort Hussein, S. B.
title Hybrid intelligent active force controller for robot arms using evolutionary neural networks
title_short Hybrid intelligent active force controller for robot arms using evolutionary neural networks
title_full Hybrid intelligent active force controller for robot arms using evolutionary neural networks
title_fullStr Hybrid intelligent active force controller for robot arms using evolutionary neural networks
title_full_unstemmed Hybrid intelligent active force controller for robot arms using evolutionary neural networks
title_sort hybrid intelligent active force controller for robot arms using evolutionary neural networks
publisher IEEE, Piscataway, NJ, United States
publishDate 2000
url http://eprints.utm.my/id/eprint/7086/
http://ieeexplore.ieee.org/document/870284/
_version_ 1643644698871463936
score 13.160551