Control of flexible-link manipulators using neural networks:
Control of Flexible-link Manipulators Using Neural Networks addresses the difficulties that arise in controlling the end-point of a manipulator that has a significant amount of structural flexibility in its links. The non-minimum phase characteristic, coupling effects, nonlinearities, parameter vari...
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| Hauptverfasser: | , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London
Springer London
2001
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| Schriftenreihe: | Lecture Notes in Control and Information Sciences
261 |
| Schlagworte: | |
| Online-Zugang: | FHI01 BTU01 Volltext |
| Zusammenfassung: | Control of Flexible-link Manipulators Using Neural Networks addresses the difficulties that arise in controlling the end-point of a manipulator that has a significant amount of structural flexibility in its links. The non-minimum phase characteristic, coupling effects, nonlinearities, parameter variations and unmodeled dynamics in such a manipulator all contribute to these difficulties. Control strategies that ignore these uncertainties and nonlinearities generally fail to provide satisfactory closed-loop performance. This monograph develops and experimentally evaluates several intelligent (neural network based) control techniques to address the problem of controlling the end-point of flexible-link manipulators in the presence of all the aforementioned difficulties. To highlight the main issues, a very flexible-link manipulator whose hub exhibits a considerable amount of friction is considered for the experimental work. Four different neural network schemes are proposed and implemented on the experimental test-bed. The neural networks are trained and employed as online controllers |
| Beschreibung: | 1 Online-Ressource (XVIII, 150 p) |
| ISBN: | 9781846285721 |
| DOI: | 10.1007/BFb0110411 |
Internformat
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| 520 | |a Control of Flexible-link Manipulators Using Neural Networks addresses the difficulties that arise in controlling the end-point of a manipulator that has a significant amount of structural flexibility in its links. The non-minimum phase characteristic, coupling effects, nonlinearities, parameter variations and unmodeled dynamics in such a manipulator all contribute to these difficulties. Control strategies that ignore these uncertainties and nonlinearities generally fail to provide satisfactory closed-loop performance. This monograph develops and experimentally evaluates several intelligent (neural network based) control techniques to address the problem of controlling the end-point of flexible-link manipulators in the presence of all the aforementioned difficulties. To highlight the main issues, a very flexible-link manipulator whose hub exhibits a considerable amount of friction is considered for the experimental work. Four different neural network schemes are proposed and implemented on the experimental test-bed. The neural networks are trained and employed as online controllers | ||
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Datensatz im Suchindex
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| any_adam_object | |
| author | Talebi, H. A. Patel, R. V. Khorasani, K. |
| author_facet | Talebi, H. A. Patel, R. V. Khorasani, K. |
| author_role | aut aut aut |
| author_sort | Talebi, H. A. |
| author_variant | h a t ha hat r v p rv rvp k k kk |
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| bvnumber | BV045149093 |
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| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
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| dewey-search | 629.8 |
| dewey-sort | 3629.8 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Mathematik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| doi_str_mv | 10.1007/BFb0110411 |
| format | Electronic eBook |
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| id | DE-604.BV045149093 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:02Z |
| institution | BVB |
| isbn | 9781846285721 |
| language | English |
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| spelling | Talebi, H. A. Verfasser aut Control of flexible-link manipulators using neural networks by H. A. Talebi, R. V. Patel, K. Khorasani London Springer London 2001 1 Online-Ressource (XVIII, 150 p) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Control and Information Sciences 261 Control of Flexible-link Manipulators Using Neural Networks addresses the difficulties that arise in controlling the end-point of a manipulator that has a significant amount of structural flexibility in its links. The non-minimum phase characteristic, coupling effects, nonlinearities, parameter variations and unmodeled dynamics in such a manipulator all contribute to these difficulties. Control strategies that ignore these uncertainties and nonlinearities generally fail to provide satisfactory closed-loop performance. This monograph develops and experimentally evaluates several intelligent (neural network based) control techniques to address the problem of controlling the end-point of flexible-link manipulators in the presence of all the aforementioned difficulties. To highlight the main issues, a very flexible-link manipulator whose hub exhibits a considerable amount of friction is considered for the experimental work. Four different neural network schemes are proposed and implemented on the experimental test-bed. The neural networks are trained and employed as online controllers Engineering Control, Robotics, Mechatronics Control engineering Robotics Mechatronics Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Dynamisches Modell (DE-588)4150932-8 gnd rswk-swf Reglerentwurf (DE-588)4177447-4 gnd rswk-swf Manipulator (DE-588)4037349-6 gnd rswk-swf Manipulator (DE-588)4037349-6 s Neuronales Netz (DE-588)4226127-2 s 1\p DE-604 Reglerentwurf (DE-588)4177447-4 s 2\p DE-604 Dynamisches Modell (DE-588)4150932-8 s 3\p DE-604 Maschinelles Lernen (DE-588)4193754-5 s 4\p DE-604 Patel, R. V. aut Khorasani, K. aut Erscheint auch als Druck-Ausgabe 9781852334093 https://doi.org/10.1007/BFb0110411 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 4\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Talebi, H. A. Patel, R. V. Khorasani, K. Control of flexible-link manipulators using neural networks Engineering Control, Robotics, Mechatronics Control engineering Robotics Mechatronics Maschinelles Lernen (DE-588)4193754-5 gnd Neuronales Netz (DE-588)4226127-2 gnd Dynamisches Modell (DE-588)4150932-8 gnd Reglerentwurf (DE-588)4177447-4 gnd Manipulator (DE-588)4037349-6 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4226127-2 (DE-588)4150932-8 (DE-588)4177447-4 (DE-588)4037349-6 |
| title | Control of flexible-link manipulators using neural networks |
| title_auth | Control of flexible-link manipulators using neural networks |
| title_exact_search | Control of flexible-link manipulators using neural networks |
| title_full | Control of flexible-link manipulators using neural networks by H. A. Talebi, R. V. Patel, K. Khorasani |
| title_fullStr | Control of flexible-link manipulators using neural networks by H. A. Talebi, R. V. Patel, K. Khorasani |
| title_full_unstemmed | Control of flexible-link manipulators using neural networks by H. A. Talebi, R. V. Patel, K. Khorasani |
| title_short | Control of flexible-link manipulators using neural networks |
| title_sort | control of flexible link manipulators using neural networks |
| topic | Engineering Control, Robotics, Mechatronics Control engineering Robotics Mechatronics Maschinelles Lernen (DE-588)4193754-5 gnd Neuronales Netz (DE-588)4226127-2 gnd Dynamisches Modell (DE-588)4150932-8 gnd Reglerentwurf (DE-588)4177447-4 gnd Manipulator (DE-588)4037349-6 gnd |
| topic_facet | Engineering Control, Robotics, Mechatronics Control engineering Robotics Mechatronics Maschinelles Lernen Neuronales Netz Dynamisches Modell Reglerentwurf Manipulator |
| url | https://doi.org/10.1007/BFb0110411 |
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