Strategies for Feedback Linearisation: A Dynamic Neural Network Approach
The series Advances in Industrial Control aims to report and encourage of control technology transfer in control engineering. The rapid development technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer met...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London
Springer London
2003
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| Schriftenreihe: | Advances in Industrial Control
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| Schlagworte: | |
| Online-Zugang: | FHI01 BTU01 URL des Erstveröffentlichers |
| Zusammenfassung: | The series Advances in Industrial Control aims to report and encourage of control technology transfer in control engineering. The rapid development technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Nonlinear control methods continue to exert a continuing fascination for current researchers in control systems techniques. Many industrial systems are nonlinear as was so ably demonstrated in the recent Advances in Industrial Control monograph on hydraulic servo-systems by M. Jelali and A. Kroll. However, the need to use a nonlinear control technique depends on the severity of the nonlinearity and the performance specification of the application. In some cases it is imperative that a nonlinear technique be used. The type of technique which is applied usually depends on the available information on the system description. This is the key determinant in the development of new nonlinear control methods. Over the next few years it is hoped that the nonlinear control paradigm will produce several methods which will be easily and widely applicable in industrial problems. In the meantime the search and development research go on |
| Beschreibung: | 1 Online-Ressource (XV, 171 p) |
| ISBN: | 9781447100652 |
| DOI: | 10.1007/978-1-4471-0065-2 |
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Datensatz im Suchindex
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| any_adam_object | |
| author | Garces, Freddy Becerra, Victor M. Kambhampati, Chandrasekhar Warwick, Kevin |
| author_facet | Garces, Freddy Becerra, Victor M. Kambhampati, Chandrasekhar Warwick, Kevin |
| author_role | aut aut aut aut |
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| ctrlnum | (ZDB-2-ENG)978-1-4471-0065-2 (OCoLC)1050937550 (DE-599)BVBBV045148717 |
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| discipline | Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
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| id | DE-604.BV045148717 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:01Z |
| institution | BVB |
| isbn | 9781447100652 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030538416 |
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| physical | 1 Online-Ressource (XV, 171 p) |
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| publisher | Springer London |
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| series2 | Advances in Industrial Control |
| spelling | Garces, Freddy Verfasser aut Strategies for Feedback Linearisation A Dynamic Neural Network Approach by Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati, Kevin Warwick London Springer London 2003 1 Online-Ressource (XV, 171 p) txt rdacontent c rdamedia cr rdacarrier Advances in Industrial Control The series Advances in Industrial Control aims to report and encourage of control technology transfer in control engineering. The rapid development technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Nonlinear control methods continue to exert a continuing fascination for current researchers in control systems techniques. Many industrial systems are nonlinear as was so ably demonstrated in the recent Advances in Industrial Control monograph on hydraulic servo-systems by M. Jelali and A. Kroll. However, the need to use a nonlinear control technique depends on the severity of the nonlinearity and the performance specification of the application. In some cases it is imperative that a nonlinear technique be used. The type of technique which is applied usually depends on the available information on the system description. This is the key determinant in the development of new nonlinear control methods. Over the next few years it is hoped that the nonlinear control paradigm will produce several methods which will be easily and widely applicable in industrial problems. In the meantime the search and development research go on Engineering Control Artificial Intelligence (incl. Robotics) Systems Theory, Control Industrial and Production Engineering Artificial intelligence System theory Control engineering Industrial engineering Production engineering Neuronales Feld (DE-588)4642065-4 gnd rswk-swf Systemidentifikation (DE-588)4121753-6 gnd rswk-swf Nichtlineares Regelungssystem (DE-588)4276212-1 gnd rswk-swf Linearisierung (DE-588)4199872-8 gnd rswk-swf Nichtlineares Regelungssystem (DE-588)4276212-1 s Linearisierung (DE-588)4199872-8 s Neuronales Feld (DE-588)4642065-4 s Systemidentifikation (DE-588)4121753-6 s 1\p DE-604 Becerra, Victor M. aut Kambhampati, Chandrasekhar aut Warwick, Kevin aut Erscheint auch als Druck-Ausgabe 9781447110958 https://doi.org/10.1007/978-1-4471-0065-2 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Garces, Freddy Becerra, Victor M. Kambhampati, Chandrasekhar Warwick, Kevin Strategies for Feedback Linearisation A Dynamic Neural Network Approach Engineering Control Artificial Intelligence (incl. Robotics) Systems Theory, Control Industrial and Production Engineering Artificial intelligence System theory Control engineering Industrial engineering Production engineering Neuronales Feld (DE-588)4642065-4 gnd Systemidentifikation (DE-588)4121753-6 gnd Nichtlineares Regelungssystem (DE-588)4276212-1 gnd Linearisierung (DE-588)4199872-8 gnd |
| subject_GND | (DE-588)4642065-4 (DE-588)4121753-6 (DE-588)4276212-1 (DE-588)4199872-8 |
| title | Strategies for Feedback Linearisation A Dynamic Neural Network Approach |
| title_auth | Strategies for Feedback Linearisation A Dynamic Neural Network Approach |
| title_exact_search | Strategies for Feedback Linearisation A Dynamic Neural Network Approach |
| title_full | Strategies for Feedback Linearisation A Dynamic Neural Network Approach by Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati, Kevin Warwick |
| title_fullStr | Strategies for Feedback Linearisation A Dynamic Neural Network Approach by Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati, Kevin Warwick |
| title_full_unstemmed | Strategies for Feedback Linearisation A Dynamic Neural Network Approach by Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati, Kevin Warwick |
| title_short | Strategies for Feedback Linearisation |
| title_sort | strategies for feedback linearisation a dynamic neural network approach |
| title_sub | A Dynamic Neural Network Approach |
| topic | Engineering Control Artificial Intelligence (incl. Robotics) Systems Theory, Control Industrial and Production Engineering Artificial intelligence System theory Control engineering Industrial engineering Production engineering Neuronales Feld (DE-588)4642065-4 gnd Systemidentifikation (DE-588)4121753-6 gnd Nichtlineares Regelungssystem (DE-588)4276212-1 gnd Linearisierung (DE-588)4199872-8 gnd |
| topic_facet | Engineering Control Artificial Intelligence (incl. Robotics) Systems Theory, Control Industrial and Production Engineering Artificial intelligence System theory Control engineering Industrial engineering Production engineering Neuronales Feld Systemidentifikation Nichtlineares Regelungssystem Linearisierung |
| url | https://doi.org/10.1007/978-1-4471-0065-2 |
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