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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/24566
Ver los metadatos del registro completo
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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) |
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application/pdf |
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reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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alira@sedici.unlp.edu.ar |
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score |
13.13397 |