Multiple models approach in automation takagi-sugeno fuzzy systems
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 multipl...
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| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
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London : Hoboken, N.J. :
ISTE ; John Wiley and Sons Inc
2013.
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| Edición: | 1st edition |
| Colección: | Automation-control and industrial engineering series
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628545006719 |
Tabla de Contenidos:
- 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
- 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
- 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. α-stability: decay rate
- 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
- A1.2.1. Open left half-planeA1.2.2. α-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
