Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process
Nowadays, even though cognitive control architectures form an important area of research, there are many constraints on the broad application of cognitive control at an industrial level and very few systematic approaches truly inspired by biological processes, from the perspective of control enginee...
Ausführliche Beschreibung
Autor*in: |
Rodolfo E Haber [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2015 |
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Rechteinformationen: |
Nutzungsrecht: © COPYRIGHT 2015 Elsevier B.V. |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Computers in industry - Amsterdam : North Holland Publ. Co., 1979, 74(2015), Seite 135 |
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Übergeordnetes Werk: |
volume:74 ; year:2015 ; pages:135 |
Links: |
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DOI / URN: |
10.1016/j.compind.2015.05.001 |
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title_full |
Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process |
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Rodolfo E Haber |
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Computers in industry |
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Rodolfo E Haber |
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Rodolfo E Haber |
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10.1016/j.compind.2015.05.001 |
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070 |
title_sort |
artificial cognitive control with self-x capabilities: a case study of a micro-manufacturing process |
title_auth |
Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process |
abstract |
Nowadays, even though cognitive control architectures form an important area of research, there are many constraints on the broad application of cognitive control at an industrial level and very few systematic approaches truly inspired by biological processes, from the perspective of control engineering. Thus, our main purpose here is the emulation of human socio-cognitive skills, so as to approach control engineering problems in an effective way at an industrial level. The artificial cognitive control architecture that we propose, based on the shared circuits model of socio-cognitive skills, seeks to overcome limitations from the perspectives of computer science, neuroscience and systems engineering. The design and implementation of artificial cognitive control architecture is focused on four key areas: (i) self-optimization and self-leaning capabilities by estimation of distribution and reinforcement-learning mechanisms; (ii) portability and scalability based on low-cost computing platforms; (iii) connectivity based on middleware; and (iv) model-driven approaches. The results of simulation and real-time application to force control of micro-manufacturing processes are presented as a proof of concept. The proof of concept of force control yields good transient responses, short settling times and acceptable steady-state error. The artificial cognitive control architecture built into a low-cost computing platform demonstrates the suitability of its implementation in an industrial setup. |
abstractGer |
Nowadays, even though cognitive control architectures form an important area of research, there are many constraints on the broad application of cognitive control at an industrial level and very few systematic approaches truly inspired by biological processes, from the perspective of control engineering. Thus, our main purpose here is the emulation of human socio-cognitive skills, so as to approach control engineering problems in an effective way at an industrial level. The artificial cognitive control architecture that we propose, based on the shared circuits model of socio-cognitive skills, seeks to overcome limitations from the perspectives of computer science, neuroscience and systems engineering. The design and implementation of artificial cognitive control architecture is focused on four key areas: (i) self-optimization and self-leaning capabilities by estimation of distribution and reinforcement-learning mechanisms; (ii) portability and scalability based on low-cost computing platforms; (iii) connectivity based on middleware; and (iv) model-driven approaches. The results of simulation and real-time application to force control of micro-manufacturing processes are presented as a proof of concept. The proof of concept of force control yields good transient responses, short settling times and acceptable steady-state error. The artificial cognitive control architecture built into a low-cost computing platform demonstrates the suitability of its implementation in an industrial setup. |
abstract_unstemmed |
Nowadays, even though cognitive control architectures form an important area of research, there are many constraints on the broad application of cognitive control at an industrial level and very few systematic approaches truly inspired by biological processes, from the perspective of control engineering. Thus, our main purpose here is the emulation of human socio-cognitive skills, so as to approach control engineering problems in an effective way at an industrial level. The artificial cognitive control architecture that we propose, based on the shared circuits model of socio-cognitive skills, seeks to overcome limitations from the perspectives of computer science, neuroscience and systems engineering. The design and implementation of artificial cognitive control architecture is focused on four key areas: (i) self-optimization and self-leaning capabilities by estimation of distribution and reinforcement-learning mechanisms; (ii) portability and scalability based on low-cost computing platforms; (iii) connectivity based on middleware; and (iv) model-driven approaches. The results of simulation and real-time application to force control of micro-manufacturing processes are presented as a proof of concept. The proof of concept of force control yields good transient responses, short settling times and acceptable steady-state error. The artificial cognitive control architecture built into a low-cost computing platform demonstrates the suitability of its implementation in an industrial setup. |
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title_short |
Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process |
url |
http://dx.doi.org/10.1016/j.compind.2015.05.001 http://search.proquest.com/docview/1729122285 |
remote_bool |
false |
author2 |
Carmelo Juanes Raúl del Toro Gerardo Beruvides |
author2Str |
Carmelo Juanes Raúl del Toro Gerardo Beruvides |
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up_date |
2024-07-04T02:50:56.967Z |
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