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210123 ||| eng |
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|a 1118577329
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|a 9781118577325
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|a 1299186599
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|a 9781118577295
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|a 1118577299
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|a 1118577221
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|a 9781118577226
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4 |
|a T59.5
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|a Chadli, Mohammed
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|a Multiple models approach in automation
|b takagi-sugeno fuzzy systems
|c Mohammed Chadli, Pierre Borne ; series editor, Bernard Dubuisson
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| 260 |
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|a London
|b ISTE
|c 2013
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| 300 |
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|a xv, 186 pages
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|a Includes bibliographical references and index
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|a Chapter 2. Stability of Continuous Multiple Models2.1. Introduction; 2.2. Stability analysis; 2.2.1. Exponential stability; 2.3. Relaxed stability; 2.4. Example; 2.5. Robust stability; 2.5.1. Norm-bounded uncertainties; 2.5.2. Structured parametric uncertainties; 2.5.3. Analysis of nominal stability; 2.5.4. Analysis of robust stability; 2.6. Conclusion; Chapter 3. Multiple Model State Estimation; 3.1. Introduction; 3.2. Synthesis of multiple observers; 3.2.1. Linearization; 3.2.2. Pole placement; 3.2.3. Application: asynchronous machine; 3.2.4. Synthesis of multiple observers
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|a Title Page; Contents; Notations; Introduction; Chapter 1. Multiple Model Representation; 1.1. Introduction; 1.2. Techniques for obtaining multiple models; 1.2.1. Construction of multiple models by identification; 1.2.2. Multiple model construction by linearization; 1.2.3. Multiple model construction by mathematical transformation; 1.2.4. Multiple model representation using the neural approach; 1.3. Analysis and synthesis tools; 1.3.1. Lyapunov approach; 1.3.2. Numeric tools: linear matrix inequalities; 1.3.3. Multiple model control techniques
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|a 4.3. Observer-based controller4.3.1. Unmeasurable decision variables; 4.4. Static output feedback control; 4.4.1. Pole placement; 4.5. Conclusion; Chapter 5. Robust Stabilization of Multiple Models; 5.1. Introduction; 5.2. State feedback control.; 5.2.1. Norm-bounded uncertainties; 5.2.2. Interval uncertainties; 5.3. Output feedback control; 5.3.1. Norm-bounded uncertainties; 5.3.2. Interval uncertainties; 5.4. Observer-based control; 5.5. Conclusion; Conclusion; APPENDICES; Appendix 1: LMI Regions; A1.1. Definition of an LMI region; A1.2. Interesting LMI region examples
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| 653 |
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|a Fuzzy systems / http://id.loc.gov/authorities/subjects/sh85052628
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| 653 |
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|a Fuzzy systems / fast
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| 653 |
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|a Automation / fast
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| 653 |
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|a Automation / http://id.loc.gov/authorities/subjects/sh85010116
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|a Systèmes flous
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| 653 |
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|a automation / aat
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| 653 |
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|a Automatisation
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| 700 |
1 |
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|a Borne, Pierre
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| 700 |
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|a Dubuisson, Bernard
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 490 |
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|a Automation-control and industrial engineering series
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| 776 |
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|z 9781118577295
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| 776 |
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|z 9781848214125
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| 776 |
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|z 1118577299
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781118577226/?ar
|x Verlag
|3 Volltext
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|a 600
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|a 003.75
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|a Much work on analysis and synthesis problems relating to the multiple model approach has already been undertaken. This has been motivated by the desire to establish the problems of control law synthesis and full state estimation in numerical terms.In recent years, a general approach based on multiple LTI models (linear or affine) around various function points has been proposed. This so-called multiple model approach is a convex polytopic representation, which can be obtained either directly from a nonlinear mathematical model, through mathematical transformation or through linearizat
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