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
Gespeichert in:
| 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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| 520 | |a 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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10.1016/j.compind.2015.05.001 doi PQ20160617 (DE-627)OLC1968205209 (DE-599)GBVOLC1968205209 (PRQ)g1991-6bf311169ca1aae6db737ec8dc75b852c3f7ad44c1336c68446f88791a5e3c970 (KEY)0095155220150000074000000135artificialcognitivecontrolwithselfxcapabilitiesaca DE-627 ger DE-627 rakwb eng 070 DNB Rodolfo E Haber verfasserin aut Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier 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. Nutzungsrecht: © COPYRIGHT 2015 Elsevier B.V. Artificial intelligence Scalability Studies Circuits Manufacturing Computer science Software engineering Carmelo Juanes oth Raúl del Toro oth Gerardo Beruvides oth Enthalten in Computers in industry Amsterdam : North Holland Publ. Co., 1979 74(2015), Seite 135 (DE-627)130078123 (DE-600)445630-0 (DE-576)015614891 0166-3615 nnns volume:74 year:2015 pages:135 http://dx.doi.org/10.1016/j.compind.2015.05.001 Volltext http://search.proquest.com/docview/1729122285 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_26 GBV_ILN_70 GBV_ILN_4029 AR 74 2015 135 |
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Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process |
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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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10.1016/j.compind.2015.05.001 |
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artificial cognitive control with self-x capabilities: a case study of a micro-manufacturing process |
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Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process |
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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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Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process |
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Carmelo Juanes Raúl del Toro Gerardo Beruvides |
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