Robot arm fuzzy control by a neuro-genetic algorithm

Autores
Kavka, Carlos; Crespo, María Liz
Año de publicación
1998
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Robot arm control is a difficult problem. Fuzzy controllers have been applied succesfully to this control task. However, the definition of the rule base and the membership functions is itself a big problem. In this paper, an extension of a previously proposed algorithm based on neuro-genetic techniques is introduced and evaluated in a robot arm control problem. The extended algorithm can be used to generate a complete fuzzy rule base from scratch, and to define the number and shape of the membership functions of the output variables. However, in most control tasks, there are some rules and some membership functions that are obvious and can be defined manually. The algorithm can be used to extend this minimal set of fuzzy rules and membership functions, by adding new rules and new membership functions as needed. A neural network based algorithm can then be used to enhance the quality of the fuzzy controllers, by fine tuning the membership functions. The approach was evaluated in control tasks by using a robot emulator of a Philips Puma like robot called OSCAR. The fuzzy controllers generated showed to be very effective to control the arm. A complete graphical development system, together with the emulator and examples is available in Internet.
Sistemas Inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Informática
Neural nets
Robotics
Network management
robot arm control
fuzzy controllers
neural networks
evolutionary algorithms
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/24566

id SEDICI_50b44c51ee4a00fb2c406558ed9c00d0
oai_identifier_str oai:sedici.unlp.edu.ar:10915/24566
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Robot arm fuzzy control by a neuro-genetic algorithmKavka, CarlosCrespo, María LizCiencias InformáticasInformáticaNeural netsRoboticsNetwork managementrobot arm controlfuzzy controllersneural networksevolutionary algorithmsRobot arm control is a difficult problem. Fuzzy controllers have been applied succesfully to this control task. However, the definition of the rule base and the membership functions is itself a big problem. In this paper, an extension of a previously proposed algorithm based on neuro-genetic techniques is introduced and evaluated in a robot arm control problem. The extended algorithm can be used to generate a complete fuzzy rule base from scratch, and to define the number and shape of the membership functions of the output variables. However, in most control tasks, there are some rules and some membership functions that are obvious and can be defined manually. The algorithm can be used to extend this minimal set of fuzzy rules and membership functions, by adding new rules and new membership functions as needed. A neural network based algorithm can then be used to enhance the quality of the fuzzy controllers, by fine tuning the membership functions. The approach was evaluated in control tasks by using a robot emulator of a Philips Puma like robot called OSCAR. The fuzzy controllers generated showed to be very effective to control the arm. A complete graphical development system, together with the emulator and examples is available in Internet.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI)1998-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/24566enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:28:40Zoai:sedici.unlp.edu.ar:10915/24566Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:28:40.496SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Robot arm fuzzy control by a neuro-genetic algorithm
title Robot arm fuzzy control by a neuro-genetic algorithm
spellingShingle Robot arm fuzzy control by a neuro-genetic algorithm
Kavka, Carlos
Ciencias Informáticas
Informática
Neural nets
Robotics
Network management
robot arm control
fuzzy controllers
neural networks
evolutionary algorithms
title_short Robot arm fuzzy control by a neuro-genetic algorithm
title_full Robot arm fuzzy control by a neuro-genetic algorithm
title_fullStr Robot arm fuzzy control by a neuro-genetic algorithm
title_full_unstemmed Robot arm fuzzy control by a neuro-genetic algorithm
title_sort Robot arm fuzzy control by a neuro-genetic algorithm
dc.creator.none.fl_str_mv Kavka, Carlos
Crespo, María Liz
author Kavka, Carlos
author_facet Kavka, Carlos
Crespo, María Liz
author_role author
author2 Crespo, María Liz
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Informática
Neural nets
Robotics
Network management
robot arm control
fuzzy controllers
neural networks
evolutionary algorithms
topic Ciencias Informáticas
Informática
Neural nets
Robotics
Network management
robot arm control
fuzzy controllers
neural networks
evolutionary algorithms
dc.description.none.fl_txt_mv Robot arm control is a difficult problem. Fuzzy controllers have been applied succesfully to this control task. However, the definition of the rule base and the membership functions is itself a big problem. In this paper, an extension of a previously proposed algorithm based on neuro-genetic techniques is introduced and evaluated in a robot arm control problem. The extended algorithm can be used to generate a complete fuzzy rule base from scratch, and to define the number and shape of the membership functions of the output variables. However, in most control tasks, there are some rules and some membership functions that are obvious and can be defined manually. The algorithm can be used to extend this minimal set of fuzzy rules and membership functions, by adding new rules and new membership functions as needed. A neural network based algorithm can then be used to enhance the quality of the fuzzy controllers, by fine tuning the membership functions. The approach was evaluated in control tasks by using a robot emulator of a Philips Puma like robot called OSCAR. The fuzzy controllers generated showed to be very effective to control the arm. A complete graphical development system, together with the emulator and examples is available in Internet.
Sistemas Inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description Robot arm control is a difficult problem. Fuzzy controllers have been applied succesfully to this control task. However, the definition of the rule base and the membership functions is itself a big problem. In this paper, an extension of a previously proposed algorithm based on neuro-genetic techniques is introduced and evaluated in a robot arm control problem. The extended algorithm can be used to generate a complete fuzzy rule base from scratch, and to define the number and shape of the membership functions of the output variables. However, in most control tasks, there are some rules and some membership functions that are obvious and can be defined manually. The algorithm can be used to extend this minimal set of fuzzy rules and membership functions, by adding new rules and new membership functions as needed. A neural network based algorithm can then be used to enhance the quality of the fuzzy controllers, by fine tuning the membership functions. The approach was evaluated in control tasks by using a robot emulator of a Philips Puma like robot called OSCAR. The fuzzy controllers generated showed to be very effective to control the arm. A complete graphical development system, together with the emulator and examples is available in Internet.
publishDate 1998
dc.date.none.fl_str_mv 1998-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/24566
url http://sedici.unlp.edu.ar/handle/10915/24566
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1842260126412570624
score 13.13397