LEADER 05475nam 2200649 a 450 001 9910818728503321 005 20230803023846.0 010 $a1-118-65097-2 010 $a1-118-65098-0 010 $a1-118-65096-4 035 $a(CKB)2560000000103656 035 $a(EBL)1213812 035 $a(OCoLC)851316215 035 $a(DLC) 2013018480 035 $a(OCoLC)842337745 035 $a(MiAaPQ)EBC1213812 035 $a(Au-PeEL)EBL1213812 035 $a(CaPaEBR)ebr10719141 035 $a(CaONFJC)MIL497783 035 $a(EXLCZ)992560000000103656 100 $a20150303d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aFiltering, control, and fault detection with randomly occurring incomplete information /$fHongli Dong, Zidong Wang, Huijun Gao 205 $a1st ed. 210 $aChichester, West Sussex, U.K. $cWiley$dc2013 215 $a1 online resource (283 p.) 300 $aDescription based upon print version of record. 311 $a1-118-64791-2 320 $aIncludes bibliographical references and index. 327 $aFILTERING, CONTROL AND FAULT DETECTION WITH RANDOMLY OCCURRING INCOMPLETE INFORMATION; Contents; Preface; Acknowledgments; List of Abbreviations; List of Notations; 1 Introduction; 1.1 Background, Motivations, and Research Problems; 1.1.1 Randomly Occurring Incomplete Information; 1.1.2 The Analysis and Synthesis of Nonlinear Stochastic Systems; 1.1.3 Distributed Filtering over Sensor Networks; 1.2 Outline; 2 Variance-Constrained Finite-Horizon Filtering and Control with Saturations; 2.1 Problem Formulation for Finite-Horizon Filter Design; 2.2 Analysis of H and Covariance Performances 327 $a2.2.1 H Performance2.2.2 Variance Analysis; 2.3 Robust Finite-Horizon Filter Design; 2.4 Robust H Finite-Horizon Control with Sensor and Actuator Saturations; 2.4.1 Problem Formulation; 2.4.2 Main Results; 2.5 Illustrative Examples; 2.5.1 Example 1; 2.5.2 Example 2; 2.6 Summary; 3 Filtering and Control with Stochastic Delays and Missing Measurements; 3.1 Problem Formulation for Robust Filter Design; 3.2 Robust H Filtering Performance Analysis; 3.3 Robust H Filter Design; 3.4 Robust H Fuzzy Control; 3.4.1 Problem Formulation; 3.4.2 Performance Analysis; 3.4.3 Controller Design 327 $a3.5 Illustrative Examples3.5.1 Example 1; 3.5.2 Example 2; 3.5.3 Example 3; 3.6 Summary; 4 Filtering and Control for Systems with Repeated Scalar Nonlinearities; 4.1 Problem Formulation for Filter Design; 4.1.1 The Physical Plant; 4.1.2 The Communication Link; 4.1.3 The Filter; 4.1.4 The Filtering Error Dynamics; 4.2 Filtering Performance Analysis; 4.3 Filter Design; 4.4 Observer-Based H Control with Multiple Packet Losses; 4.4.1 Problem Formulation; 4.4.2 Main Results; 4.5 Illustrative Examples; 4.5.1 Example 1; 4.5.2 Example 2; 4.5.3 Example 3; 4.5.4 Example 4; 4.6 Summary 327 $a5 Filtering and Fault Detection for Markov Systems with Varying Nonlinearities5.1 Problem Formulation for Robust H° Filter Design; 5.2 Performance Analysis of Robust H° Filter; 5.3 Design of Robust H° Filters; 5.4 Fault Detection with Sensor Saturations and Randomly Varying Nonlinearities; 5.4.1 Problem Formulation; 5.4.2 Main Results; 5.5 Illustrative Examples; 5.5.1 Example 1; 5.5.2 Example 2; 5.5.3 Example 3; 5.5.4 Example 4; 5.6 Summary; 6 Quantized Fault Detection with Mixed Time-Delays and Packet Dropouts; 6.1 Problem Formulation for Fault Detection Filter Design; 6.2 Main Results 327 $a6.3 Fuzzy-Model-Based Robust Fault Detection6.3.1 Problem Formulation; 6.3.2 Main Results; 6.4 Illustrative Examples; 6.4.1 Example 1; 6.4.2 Example 2; 6.5 Summary; 7 Distributed Filtering over Sensor Networks with Saturations; 7.1 Problem Formulation; 7.2 Main Results; 7.3 An Illustrative Example; 7.4 Summary; 8 Distributed Filtering with Quantization Errors: The Finite-Horizon Case; 8.1 Problem Formulation; 8.2 Main Results; 8.3 An Illustrative Example; 8.4 Summary; 9 Distributed Filtering for Markov Jump Nonlinear Time-Delay Systems; 9.1 Problem Formulation 327 $a9.1.1 Deficient Statistics of Markovian Modes Transitions 330 $a In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modelling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Filtering, Control and Fault Detection with Randomly Occurring Incomplete Information reflects 606 $aAutomatic control 606 $aElectric filters, Digital 606 $aFault tolerance (Engineering) 615 0$aAutomatic control. 615 0$aElectric filters, Digital. 615 0$aFault tolerance (Engineering) 676 $a629.83202855369 700 $aDong$b Hongli$f1977-$01635787 701 $aWang$b Zidong$f1966-$01635788 701 $aGao$b Huijun$0739791 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910818728503321 996 $aFiltering, control, and fault detection with randomly occurring incomplete information$93976750 997 $aUNINA