Identification and Control Using Volterra Models:
Much has been written about the general difficulty of developing the models required for model-based control of processes whose dynamics exhibit signif icant nonlinearity (for further discussion and references, see Chapter 1). In fact, the development ofthese models stands as a significant practica...
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Hauptverfasser: | , , |
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Format: | Elektronisch E-Book |
Sprache: | English |
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London
Springer London
2002
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Schriftenreihe: | Communications and Control Engineering
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Online-Zugang: | FHI01 BTU01 Volltext |
Zusammenfassung: | Much has been written about the general difficulty of developing the models required for model-based control of processes whose dynamics exhibit signif icant nonlinearity (for further discussion and references, see Chapter 1). In fact, the development ofthese models stands as a significant practical imped iment to widespread industrial application oftechniques like nonlinear model predictive control (NMPC), whoselinear counterpart has profoundly changed industrial practice. One ofthe reasons for this difficulty lies in the enormous variety of "nonlinear models," different classes of which can be less similar to each other than they are to the class of linear models. Consequently, it is a practical necessity to restrict consideration to one or a few specific nonlinear model classes if we are to succeed in developing, understanding, and using nonlinear models as a basis for practical control schemes. Because they repre sent a highly structured extension ofthe class oflinear finite impulse response (FIR) models on which industrially popular linear MPC implementations are based, this book is devoted to the class of discrete-time Volterra models and a fewother, closelyrelated, nonlinear model classes. The objective ofthis book is to provide a useful reference for researchers in the field of process control and closely related areas, collecting a reasonably wide variety of results that may be found in different parts of the large literature that exists on the gen eral topics of process control, nonlinear systems theory, statistical time-series models, biomedical engineering, and digital signal processing, among others |
Beschreibung: | 1 Online-Ressource (XIV, 314 p) |
ISBN: | 9781447101079 |
DOI: | 10.1007/978-1-4471-0107-9 |
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Datensatz im Suchindex
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any_adam_object | |
author | Doyle, F. J. Pearson, R. K. Ogunnaike, B. A. |
author_facet | Doyle, F. J. Pearson, R. K. Ogunnaike, B. A. |
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author_sort | Doyle, F. J. |
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discipline | Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
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indexdate | 2024-07-10T08:10:01Z |
institution | BVB |
isbn | 9781447101079 |
language | English |
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physical | 1 Online-Ressource (XIV, 314 p) |
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spelling | Doyle, F. J. Verfasser aut Identification and Control Using Volterra Models by F. J. Doyle, R. K. Pearson, B. A. Ogunnaike London Springer London 2002 1 Online-Ressource (XIV, 314 p) txt rdacontent c rdamedia cr rdacarrier Communications and Control Engineering Much has been written about the general difficulty of developing the models required for model-based control of processes whose dynamics exhibit signif icant nonlinearity (for further discussion and references, see Chapter 1). In fact, the development ofthese models stands as a significant practical imped iment to widespread industrial application oftechniques like nonlinear model predictive control (NMPC), whoselinear counterpart has profoundly changed industrial practice. One ofthe reasons for this difficulty lies in the enormous variety of "nonlinear models," different classes of which can be less similar to each other than they are to the class of linear models. Consequently, it is a practical necessity to restrict consideration to one or a few specific nonlinear model classes if we are to succeed in developing, understanding, and using nonlinear models as a basis for practical control schemes. Because they repre sent a highly structured extension ofthe class oflinear finite impulse response (FIR) models on which industrially popular linear MPC implementations are based, this book is devoted to the class of discrete-time Volterra models and a fewother, closelyrelated, nonlinear model classes. The objective ofthis book is to provide a useful reference for researchers in the field of process control and closely related areas, collecting a reasonably wide variety of results that may be found in different parts of the large literature that exists on the gen eral topics of process control, nonlinear systems theory, statistical time-series models, biomedical engineering, and digital signal processing, among others Engineering Control Appl.Mathematics/Computational Methods of Engineering Control, Robotics, Mechatronics Applied mathematics Engineering mathematics Control engineering Robotics Mechatronics Reglerentwurf (DE-588)4177447-4 gnd rswk-swf Systemmodell (DE-588)4304916-3 gnd rswk-swf Systemsynthese (DE-588)4198909-0 gnd rswk-swf Systemidentifikation (DE-588)4121753-6 gnd rswk-swf Volterra-Integralgleichung (DE-588)4234593-5 gnd rswk-swf Systemmodell (DE-588)4304916-3 s Systemidentifikation (DE-588)4121753-6 s Systemsynthese (DE-588)4198909-0 s Reglerentwurf (DE-588)4177447-4 s Volterra-Integralgleichung (DE-588)4234593-5 s 1\p DE-604 Pearson, R. K. aut Ogunnaike, B. A. aut Erscheint auch als Druck-Ausgabe 9781447110637 https://doi.org/10.1007/978-1-4471-0107-9 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
spellingShingle | Doyle, F. J. Pearson, R. K. Ogunnaike, B. A. Identification and Control Using Volterra Models Engineering Control Appl.Mathematics/Computational Methods of Engineering Control, Robotics, Mechatronics Applied mathematics Engineering mathematics Control engineering Robotics Mechatronics Reglerentwurf (DE-588)4177447-4 gnd Systemmodell (DE-588)4304916-3 gnd Systemsynthese (DE-588)4198909-0 gnd Systemidentifikation (DE-588)4121753-6 gnd Volterra-Integralgleichung (DE-588)4234593-5 gnd |
subject_GND | (DE-588)4177447-4 (DE-588)4304916-3 (DE-588)4198909-0 (DE-588)4121753-6 (DE-588)4234593-5 |
title | Identification and Control Using Volterra Models |
title_auth | Identification and Control Using Volterra Models |
title_exact_search | Identification and Control Using Volterra Models |
title_full | Identification and Control Using Volterra Models by F. J. Doyle, R. K. Pearson, B. A. Ogunnaike |
title_fullStr | Identification and Control Using Volterra Models by F. J. Doyle, R. K. Pearson, B. A. Ogunnaike |
title_full_unstemmed | Identification and Control Using Volterra Models by F. J. Doyle, R. K. Pearson, B. A. Ogunnaike |
title_short | Identification and Control Using Volterra Models |
title_sort | identification and control using volterra models |
topic | Engineering Control Appl.Mathematics/Computational Methods of Engineering Control, Robotics, Mechatronics Applied mathematics Engineering mathematics Control engineering Robotics Mechatronics Reglerentwurf (DE-588)4177447-4 gnd Systemmodell (DE-588)4304916-3 gnd Systemsynthese (DE-588)4198909-0 gnd Systemidentifikation (DE-588)4121753-6 gnd Volterra-Integralgleichung (DE-588)4234593-5 gnd |
topic_facet | Engineering Control Appl.Mathematics/Computational Methods of Engineering Control, Robotics, Mechatronics Applied mathematics Engineering mathematics Control engineering Robotics Mechatronics Reglerentwurf Systemmodell Systemsynthese Systemidentifikation Volterra-Integralgleichung |
url | https://doi.org/10.1007/978-1-4471-0107-9 |
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