Machine learning control: taming nonlinear dynamics and turbulence
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
[2017]
|
| Schriftenreihe: | Fluid mechanics and its applications
116 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xx, 211 Seiten Illustrationen, Diagramme |
| ISBN: | 9783319406244 9783319406237 |
Internformat
MARC
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| 100 | 1 | |a Duriez, Thomas |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Machine learning control |b taming nonlinear dynamics and turbulence |c Thomas Duriez, Steven L. Brunton, Bernd R. Noack |
| 264 | 1 | |a Cham |b Springer |c [2017] | |
| 300 | |a xx, 211 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Fluid mechanics and its applications |v 116 | |
| 650 | 4 | |a Engineering | |
| 650 | 4 | |a Microprogramming | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Fluids | |
| 650 | 4 | |a Fluid mechanics | |
| 650 | 4 | |a Control engineering | |
| 650 | 4 | |a Engineering Fluid Dynamics | |
| 650 | 4 | |a Fluid- and Aerodynamics | |
| 650 | 4 | |a Control | |
| 650 | 4 | |a Control Structures and Microprogramming | |
| 650 | 4 | |a Artificial Intelligence (incl. Robotics) | |
| 650 | 4 | |a Applications of Nonlinear Dynamics and Chaos Theory | |
| 650 | 4 | |a Ingenieurwissenschaften | |
| 650 | 4 | |a Künstliche Intelligenz | |
| 650 | 0 | 7 | |a Nichtlineare Dynamik |0 (DE-588)4126141-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Turbulente Strömung |0 (DE-588)4117265-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Kontrolltheorie |0 (DE-588)4032317-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
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| 689 | 0 | 2 | |a Nichtlineare Dynamik |0 (DE-588)4126141-0 |D s |
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| 700 | 1 | |a Brunton, Steven L. |d 1984- |e Verfasser |0 (DE-588)1125029617 |4 aut | |
| 700 | 1 | |a Noack, Bernd R. |d 1966- |e Verfasser |0 (DE-588)172554446 |4 aut | |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-029454666 | ||
Datensatz im Suchindex
| _version_ | 1804177063110770688 |
|---|---|
| adam_text | Contents 1 Introduction .......................................... ....................................................... 1.1 Feedback in Engineering and Living Systems........................ 1.2 Benefits of Feedback Control .............................. ... ... 1.3 Challenges of Feedback Control .......................................... 1.4 Feedback Turbulence Control is a Grand Challenge Problem .... 1.5 Nature Teaches Us the Control Design........................ 1.6 Outline of the Book................................................ .......................... . 1.7 Exercises................. 1 1 3 6 7 8 9 9 2 Machine Learning Control (MLC).......................................................... 2.1 Methods of Machine Learning...................... ..................................... 2.1.1 System Identification as Machine Learning........................... 2.1.2 Genetic Algorithms . . ............... 2.1.3 Genetic Programming . ............... .............. . . . .................... 2.1.4 Additional Machine Learning Methods ...... ................. 2.2 MLC with Genetic Programming. . . .................... 2.2.1 Control Problem............... . ... ............................................. 2.2.2 Parameterization of the Control Law........................... . . . . 2.2.3 Genetic Programming as a Search Algorithm ....................... 2.2.4 Initializing a Generation........................ 2.2.5 Evaluating a Generation. . ................................................. 2.2.6 Selecting Individuals for Genetic Operations............. 2.2.7 Selecting
Genetic Operations................... 2.2.8 Advancing Generations and Stopping Criteria. . ........ 2.3 Examples.......... ..................... 2.3.1 Fitting a Function Through Data Points ................................. 2.3.2 MLC Applied to Control a Dynamical System............ .. 2.4 Exercises............................. 2.5 Suggested Reading ................. 2.6 Interview with Professor Marc Schoenauer..... . ......................... 11 12 13 14 16 18 19 19 20 21 23 24 26 27 30 33 33 36 44 45 46 xi
xii Contents 3 Methods of Linear Control Theory........................................................... 3.1 Linear Systems ................................................................... 3.2 Full-State Feedback....................................... 3.3 Sensor-Based State Estimation............ .............................. 3.4 Sensor-Based Feedback................... 3.5 System Identification and Model Reduction..................................... 3.5.1 System Identification ............................................ .. ............... 3.5.2 Eigensystem Realization Algorithm (ERA)........................... 3.5.3 Observer Kalman Filter Identification (OKID)............ .. 3.6 Exercises...................... 3.7 Suggested Reading............................................................................... 49 50 51 53 56 58 59 59 62 65 67 4 Benchmarking MLC Against Linear Control........................................ 4.1 Comparison of MLC with LQR on a Linear Oscillator.................... 4.2 Comparison of MLC with Kalman Filter on a Noisy Linear Oscillator................................ 4.3 Comparison of MLC with LQG for Sensor-Based Feedback.......... 4.4 Modifications for Small Nonlinearity ................. 4.5 Exercises........................ 4.6 Interview with Professor Shervin Bagheri.......................................... 69 70 73 80 84 86 89 5 Taming Nonlinear Dynamics with MLC................................................. 93 5.1 Generalized Mean-Field System......................................................... 94 5.2 Machine Learning
Control............ .................................. 98 5.2.1 Formulation of the Control Problem ................................... .. 98 5.2.2 MLC Parameters. . .................................................... .............. 99 5.2.3 MLC Results ......................... ................................................... 99 5.3 Derivation Outline for the Generalized Mean-FieldModel.............. 105 5.4 Alternative Control Approaches. . ............... 109 5.4.1 Open-Loop Forcing........................... 109 5.4.2 Closed-Loop Forcing...................... .. ...................................... Ill 5.4.3 Short-Term Forcing.................... .. .......................................... 113 5.5 Exercises................... 115 5.6 Suggested Reading..................... . . .................................................. 116 5.7 Interview with Professor Mark N. Glauser ........................................ 117 6 Taming Real World Flow Control Experiments with MLC ............... 6.1 Separation Control Over a Backward-Facing Step . .......................... 6.1.1 Flow Over a Backward-Facing Step...................... .............. 6.1.2 Experimental Setup at PMMH............................................... 6.1.3 Results. .......................... ....................... 6.2 Separation Control of Turbulent Boundary Layers........................... 6.2.1 Separating Boundary Layers.......... ............................... .. 6.2.2 Experimental Setups at LML and PRISME. ......................... 6.2.3 Results................. .
.................................................................. 121 122 122 123 127 128 128 129 132
Contents 6.3 Control of Mixing Layer Growth....................................... 6.3.1 Mixing Layer Flows ................................................................. 6.3.2 Experimental Setup of the TUCOROM Wind Tunnel .... 6.3.3 Results........................ 6.4 Alternative Model-Based Control Approaches............... 6.5 Implementation of MLC in Experiments............................................. 6.5.1 Real-Time Control Loop—from Sensors to Actuators .... 6.5.2 MLC Implementation in the PMMH Flow Over a Backward-Facing Step........................................................... 6.5.3 MLC Implementation in the LML and PRISME Experiments..................................................................... 6.5.4 MLC Implementation in the TUCOROM Experiment .... 6.6 Suggested Reading ................................................................................ 6.7 Interview with Professor David Williams.......................................... 7 MLC Tactics and Strategy......................................................................... 7.1 The Ideal Flow Control Experiment................................................... 7.2 Desiderata of the Control Problem—From the Definition to Hardware Choices................. 7.2.1 Cost Function............................................................................. 7.2.2 Actuators...................... 7.2.3 Sensors............ ......................................................................... 7.2.4 Search Space for Control Laws............................................... 7.3 Time Scales of
MLC ............................................................................. 7.3.1 Controller............................................................................. 7.3.2 Response Time of the Plant.................................................... 7.3.3 Learning Time for MLC......................................................... 7.4 MLC Parameters and Convergence............................................ 7.4.1 Convergence Process and Its Diagnostics. ......................... . 7.4.2 Parameters.................................................................................. 7.4.3 Pre-evaluation....................................... 7.5 The Imperfect Experiment................................................................... 7.5.1 Noise. .......................................................................................... 7.5.2 Drift . ............... . . ................................................................... 7.5.3 Monitoring.................................................................................. xiii 135 135 136 138 140 143 143 144 145 146 147 149 153 153 154 155 156 156 157 158 158 160 161 162 162 165 166 167 167 167 168 8 Future Developments...................................................................................... 169 8.1 Methodological Advances of MLC............................................ 170 8.2 System-Reduction Techniques for MLC—Coping with High-Dimensional Input and Output.................................................... 174 8.3 Future Applications of MLC.
.............................................................. 176
Contents xiv 8.4 Exercises............................. 182 8.5 Interview with Professor Belinda Batten... ...................................... 184 Glossary............................. 189 Matlab® Code: OpenMLC . .·... ■.......... ·.·.................. .. ................................ 195 References.............................................................................................. 197 Index...................... ......................................... .. . . ............................................ 209
|
| any_adam_object | 1 |
| author | Duriez, Thomas Brunton, Steven L. 1984- Noack, Bernd R. 1966- |
| author_GND | (DE-588)1125029617 (DE-588)172554446 |
| author_facet | Duriez, Thomas Brunton, Steven L. 1984- Noack, Bernd R. 1966- |
| author_role | aut aut aut |
| author_sort | Duriez, Thomas |
| author_variant | t d td s l b sl slb b r n br brn |
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| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 620 - Engineering and allied operations |
| dewey-raw | 620.1064 |
| dewey-search | 620.1064 |
| dewey-sort | 3620.1064 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Physik |
| format | Book |
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| id | DE-604.BV044047768 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:42:07Z |
| institution | BVB |
| isbn | 9783319406244 9783319406237 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029454666 |
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| physical | xx, 211 Seiten Illustrationen, Diagramme |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Springer |
| record_format | marc |
| series | Fluid mechanics and its applications |
| series2 | Fluid mechanics and its applications |
| spelling | Duriez, Thomas Verfasser aut Machine learning control taming nonlinear dynamics and turbulence Thomas Duriez, Steven L. Brunton, Bernd R. Noack Cham Springer [2017] xx, 211 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Fluid mechanics and its applications 116 Engineering Microprogramming Artificial intelligence Fluids Fluid mechanics Control engineering Engineering Fluid Dynamics Fluid- and Aerodynamics Control Control Structures and Microprogramming Artificial Intelligence (incl. Robotics) Applications of Nonlinear Dynamics and Chaos Theory Ingenieurwissenschaften Künstliche Intelligenz Nichtlineare Dynamik (DE-588)4126141-0 gnd rswk-swf Turbulente Strömung (DE-588)4117265-6 gnd rswk-swf Kontrolltheorie (DE-588)4032317-1 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Kontrolltheorie (DE-588)4032317-1 s Nichtlineare Dynamik (DE-588)4126141-0 s Turbulente Strömung (DE-588)4117265-6 s DE-604 Brunton, Steven L. 1984- Verfasser (DE-588)1125029617 aut Noack, Bernd R. 1966- Verfasser (DE-588)172554446 aut Erscheint auch als Online-Ausgabe, ebook 978-3-319-40624-4 Fluid mechanics and its applications 116 (DE-604)BV035417894 116 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029454666&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Duriez, Thomas Brunton, Steven L. 1984- Noack, Bernd R. 1966- Machine learning control taming nonlinear dynamics and turbulence Fluid mechanics and its applications Engineering Microprogramming Artificial intelligence Fluids Fluid mechanics Control engineering Engineering Fluid Dynamics Fluid- and Aerodynamics Control Control Structures and Microprogramming Artificial Intelligence (incl. Robotics) Applications of Nonlinear Dynamics and Chaos Theory Ingenieurwissenschaften Künstliche Intelligenz Nichtlineare Dynamik (DE-588)4126141-0 gnd Turbulente Strömung (DE-588)4117265-6 gnd Kontrolltheorie (DE-588)4032317-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4126141-0 (DE-588)4117265-6 (DE-588)4032317-1 (DE-588)4193754-5 |
| title | Machine learning control taming nonlinear dynamics and turbulence |
| title_auth | Machine learning control taming nonlinear dynamics and turbulence |
| title_exact_search | Machine learning control taming nonlinear dynamics and turbulence |
| title_full | Machine learning control taming nonlinear dynamics and turbulence Thomas Duriez, Steven L. Brunton, Bernd R. Noack |
| title_fullStr | Machine learning control taming nonlinear dynamics and turbulence Thomas Duriez, Steven L. Brunton, Bernd R. Noack |
| title_full_unstemmed | Machine learning control taming nonlinear dynamics and turbulence Thomas Duriez, Steven L. Brunton, Bernd R. Noack |
| title_short | Machine learning control |
| title_sort | machine learning control taming nonlinear dynamics and turbulence |
| title_sub | taming nonlinear dynamics and turbulence |
| topic | Engineering Microprogramming Artificial intelligence Fluids Fluid mechanics Control engineering Engineering Fluid Dynamics Fluid- and Aerodynamics Control Control Structures and Microprogramming Artificial Intelligence (incl. Robotics) Applications of Nonlinear Dynamics and Chaos Theory Ingenieurwissenschaften Künstliche Intelligenz Nichtlineare Dynamik (DE-588)4126141-0 gnd Turbulente Strömung (DE-588)4117265-6 gnd Kontrolltheorie (DE-588)4032317-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Engineering Microprogramming Artificial intelligence Fluids Fluid mechanics Control engineering Engineering Fluid Dynamics Fluid- and Aerodynamics Control Control Structures and Microprogramming Artificial Intelligence (incl. Robotics) Applications of Nonlinear Dynamics and Chaos Theory Ingenieurwissenschaften Künstliche Intelligenz Nichtlineare Dynamik Turbulente Strömung Kontrolltheorie Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029454666&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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