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|a 827207678
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|a 9781848214125
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|z (OCoLC)828617889
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|a TK7895.E42
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|a GEN
|2 eflch
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|a HCDD
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| 100 |
1 |
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|a Chadli, M.,
|e author.
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| 245 |
1 |
0 |
|a Multiple Models Approach in Automation :
|b Takagi-Sugeno Fuzzy Systems.
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| 260 |
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|b Wiley-ISTE [Imprint],
|c Dec. 2012 ;
|a Hoboken :
|b John Wiley & Sons, Incorporated.
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| 300 |
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|a 1 online resource
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Iste Ser.
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| 520 |
8 |
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|a Annotation
|b 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 linearization around various function points. This book concentrates on the analysis of the stability and synthesis of control laws and observations for multiple models. The authors' approach is essentially based on Lyapunov's second method and LMI formulation. Uncertain multiple models with unknown inputs are studied and quadratic and non-quadratic Lyapunov functions are also considered.
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| 521 |
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|a Scholarly & Professional
|b John Wiley & Sons, Incorporated.
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| 505 |
8 |
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|a A1.2.1. Open left half-planeA1.2.2. [alpha]-stability; A1.2.3. Vertical band; A1.2.4. Horizontal band; A1.2.5. Disk of radius R, centered at (q,0); A1.2.6. Conical sector.; Appendix 2: Properties of M-Matrices; Appendix 3: Stability and Comparison Systems; A3.1. Vector norms and overvaluing systems; A3.1.1. Definition of a vector norm; A3.1.2. Definition of a system overvalued from a continuous process; A3.1.3. Application; A3.2. Vector norms and the principle of comparison; A3.3. Application to stability analysis; Bibliography; Index.
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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 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 3.3. Multiple observer for an uncertain multiple model3.4. Synthesis of unknown input observers; 3.4.1. Unknown inputs affecting system state; 3.4.2. Unknown inputs affecting system state and output; 3.4.3. Estimation of unknown inputs; 3.5. Synthesis of unknown input observers: another approach; 3.5.1. Principle; 3.5.2. Multiple observers subject to unknown inputs and uncertainties; 3.6. Conclusion; Chapter 4. Stabilization of Multiple Models; 4.1. Introduction; 4.2. Full state feedback control; 4.2.1. Linearization; 4.2.2. Specific case; 4.2.3. [alpha]-stability: decay rate.
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8 |
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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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| 650 |
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0 |
|a Nonlinear systems
|x Mathematical models.
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| 650 |
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0 |
|a Fuzzy systems.
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| 650 |
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7 |
|a Fuzzy systems
|2 fast
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| 650 |
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7 |
|a Nonlinear systems
|x Mathematical models
|2 fast
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| 700 |
1 |
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|a Chadli, Mohammed,
|e author.
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| 710 |
2 |
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|a Borne, Pierre,
|e author.
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| 758 |
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|i has work:
|a Multiple models approach in automation (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGMwWHqw8W9pykqwJFC3kP
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
8 |
|i Print version:
|a Chadli, Mohammed.
|t Multiple Models Approach in Automation : Takagi-Sugeno Fuzzy Systems.
|d London : Wiley, ©2012
|z 9781848214125
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| 830 |
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0 |
|a Iste Ser.
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| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=1120637
|y Click for online access
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| 903 |
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|a EBC-AC
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| 994 |
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|a 92
|b HCD
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