LEADER 05515nam 2200709 a 450 001 9910141495503321 005 20200520144314.0 010 $a1-118-57732-9 010 $a1-299-18659-9 010 $a1-118-57729-9 010 $a1-118-57722-1 035 $a(CKB)2670000000327632 035 $a(EBL)1120637 035 $a(SSID)ssj0000904846 035 $a(PQKBManifestationID)11530054 035 $a(PQKBTitleCode)TC0000904846 035 $a(PQKBWorkID)10924299 035 $a(PQKB)11312382 035 $a(Au-PeEL)EBL1120637 035 $a(CaPaEBR)ebr10657634 035 $a(CaONFJC)MIL449909 035 $a(CaSebORM)9781118577226 035 $a(MiAaPQ)EBC1120637 035 $a(OCoLC)828189795 035 $a(EXLCZ)992670000000327632 100 $a20120924d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMultiple models approach in automation$b[electronic resource] $etakagi-sugeno fuzzy systems /$fMohammed Chadli, Pierre Borne ; series editor, Bernard Dubuisson 205 $a1st edition 210 $aLondon $cISTE ;$aHoboken, N.J. $cJohn Wiley and Sons Inc$d2013 215 $a1 online resource (204 p.) 225 0 $aAutomation - control and industrial engineering series 300 $aDescription based upon print version of record. 311 $a1-84821-412-X 320 $aIncludes bibliographical references and index. 327 $aTitle 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 327 $aChapter 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 327 $a3.3. 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. ?-stability: decay rate 327 $a4.3. 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 327 $aA1.2.1. Open left half-planeA1.2.2. ?-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 330 $aMuch work on analysis and synthesis problems relating to the multiple model approach has already been undertaken. This has been motivated by the desire to establish the problems of control law synthesis and full state estimation in numerical terms.In recent years, a general approach based on multiple LTI models (linear or affine) around various function points has been proposed. This so-called multiple model approach is a convex polytopic representation, which can be obtained either directly from a nonlinear mathematical model, through mathematical transformation or through linearizat 410 0$aAutomation-control and industrial engineering series 606 $aAutomation 606 $aFuzzy systems 615 0$aAutomation. 615 0$aFuzzy systems. 676 $a004.1 676 $a629.80151 700 $aChadli$b Mohammed$0882294 701 $aBorne$b Pierre$060243 701 $aDubuisson$b Bernard$0741319 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141495503321 996 $aMultiple models approach in automation$91970719 997 $aUNINA