LEADER 06489nam 2200685 450 001 9910140461103321 005 20200520144314.0 010 $a1-118-92946-2 010 $a1-118-92948-9 035 $a(CKB)2670000000613967 035 $a(EBL)1895765 035 $a(SSID)ssj0001515745 035 $a(PQKBManifestationID)12623515 035 $a(PQKBTitleCode)TC0001515745 035 $a(PQKBWorkID)11482029 035 $a(PQKB)10650137 035 $a(PQKBManifestationID)16114654 035 $a(PQKB)21297284 035 $a(DLC) 2015013806 035 $a(PPN)240867025 035 $a(Au-PeEL)EBL1895765 035 $a(CaPaEBR)ebr11049052 035 $a(CaONFJC)MIL779411 035 $a(OCoLC)908519976 035 $a(CaSebORM)9781118929490 035 $a(MiAaPQ)EBC1895765 035 $a(EXLCZ)992670000000613967 100 $a20150507h20152015 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aVariance-constrained multi-objective stochastic control and filtering /$fLifeng Ma, Zidong Wang, Yuming Bo 210 1$aWest Sussex, England :$cJohn Wiley & Sons, Inc.,$d2015. 210 4$dİ2015 215 $a1 online resource (320 p.) 225 1 $aWiley Series in Dynamics and Control of Electromechanical Systems 300 $aDescription based upon print version of record. 311 $a1-118-92947-0 311 $a1-118-92949-7 320 $aIncludes bibliographical references and index. 327 $aMachine generated contents note: Preface vii Acknowledgements ix List of Abbreviations xi 1 Introduction 1 1.1 Analysis and Synthesis of Nonlinear Stochastic Systems 2 1.1.1 Nonlinear Systems 3 1.1.2 Stochastic Systems 4 1.2 Multi-Objective Control and Filtering with Variance Constraints 5 1.2.1 Covariance Control Theory 5 1.2.2 Multiple Performance Requirements 7 1.2.3 Design Techniques for Nonlinear Stochastic Systems with Variance Constraints 9 1.2.4 A Special Case of Multi-Objective Design: Mixed H2/H1 Control/Filtering 11 1.3 Outline 12 2 Robust H1 Control with Variance Constraints 17 2.1 Problem Formulation 18 2.2 Stability, H1 Performance and Variance Analysis 20 2.2.1 Stability, H1 Performance Analysis 21 2.2.2 Variance Analysis 23 2.3 Robust Controller Design 27 2.4 Numerical Example 30 2.5 Summary 33 3 Robust Mixed H2=H1 Filtering 41 3.1 System Description and Problem Formulation 42 3.2 Algebraic Characterizations for Robust H2=H1 Filtering 44 3.2.1 Robust H2 Filtering 44 3.2.2 Robust H1 Filtering 50 3.3 Robust H2=H1 Filter Design Techniques 51 3.4 An Illustrative Example 60 3.5 Summary 62 4 Filtering with Missing Measurements 63 4.1 Problem Formulation 64 4.2 Stability and Variance Analysis 67 4.3 Robust Filter Design 71 4.4 Numerical Example 75 4.5 Summary 78 5 Robust Fault-Tolerant Control 87 5.1 Problem Formulation 88 5.2 Stability and Variance Analysis 90 5.3 Robust Controller Design 92 5.4 Numerical Example 98 5.5 Summary 103 6 Robust H2 SMC 105 6.1 The System Model 106 6.2 Robust H2 Sliding Mode Control 107 6.2.1 Switching Surface 107 6.2.2 Performances of the Sliding Motion 108 6.2.3 Computational Algorithm 114 6.3 Sliding Mode Controller 115 6.4 Numerical Example 116 6.5 Summary 118 7 Dissipative Control with Degraded Measurements 125 7.1 Problem Formulation 126 7.2 Stability, Dissipativity and Variance Analysis 129 7.3 Observer-Based Controller Design 134 7.3.1 Solvability of Multi-Objective Control Problem 134 7.3.2 Computational Algorithm 139 7.4 Numerical Example 140 7.5 Summary 142 8 Variance-Constrained H1 Control with Multiplicative Noises 145 8.1 Problem Formulation 146 8.2 Stability, H1 Performance, Variance Analysis 147 8.2.1 Stability 148 8.2.2 H1 performance 150 8.2.3 Variance analysis 152 8.3 Robust State Feedback Controller Design 153 8.4 A Numerical Example 156 8.5 Summary 157 9 Robust Finite-Horizon H1 Control 159 9.1 Problem Formulation 160 9.2 Performance Analysis 162 9.2.1 H1 Performance 162 9.2.2 Variance Analysis 164 9.3 Robust Finite Horizon Controller Design 167 9.4 Numerical Example 171 9.5 Summary 173 10 Finite-Horizon Filtering with Degraded Measurements 177 10.1 Problem Formulation 178 10.2 Performance Analysis 181 10.2.1 H1 Performance Analysis 181 10.2.2 System Covariance Analysis 186 10.3 Robust Filter Design 187 10.4 Numerical Example 190 10.5 Summary 191 11 Mixed H2=H1 Control with Randomly Occurring Nonlinearities: the Finite-Horizon Case 197 11.1 Problem Formulation 199 11.2 H1 Performance 200 11.3 Mixed H2=H1 Controller Design 204 11.3.1 State-Feedback Controller Design 204 11.3.2 Computational Algorithm 207 11.4 Numerical Example 207 11.5 Summary 211 12 Finite-Horizon H2=H1 Control of MJSs with Sensor Failures 213 12.1 Problem Formulation 214 12.2 H1 Performance 216 12.3 Mixed H2=H1 Controller Design 220 12.3.1 Controller Design 220 12.3.2 Computational Algorithm 224 12.4 Numerical Example 224 12.5 Summary 227 13 Finite-Horizon Control with ROSF 229 13.1 Problem Formulation 230 13.2 H1 And Covariance Performances Analysis 234 13.2.1 H1 Performance 234 13.2.2 Covariance Analysis 238 13.3 Robust Finite-Horizon Controller Design 240 13.3.1 Controller Design 240 13.3.2 Computational Algorithm 243 13.4 Numerical Example 243 13.5 Summary 244 14 Finite-Horizon H2=H1 Control with Actuator Failures 247 14.1 Problem Formulation 248 14.2 H1 Performance 251 14.3 Multi-Objective Controller Design 253 14.3.1 Controller Design 253 14.3.2 Computational Algorithm 256 14.4 Numerical Example 257 14.5 Summary 259 15 Conclusions and Future Topics 261 References 263 . 330 $a"Variance-Constrained Multi-Objective Stochastic Control and Filtering covers the latest research results and presents the state-of-the-art of control and filtering problems while taking multiple design objectives into consideration"--$cProvided by publisher. 410 0$aWiley series in dynamics and control of electromechanical systems. 606 $aAutomatic control$xMathematics 606 $aStochastic processes 615 0$aAutomatic control$xMathematics. 615 0$aStochastic processes. 676 $a629.801/51923 700 $aMa$b Lifeng$0990147 702 $aWang$b Zidong$f1966- 702 $aBo$b Yuming 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910140461103321 996 $aVariance-constrained multi-objective stochastic control and filtering$92264791 997 $aUNINA LEADER 05647nam 22006615 450 001 996655267303316 005 20250401125300.0 010 $a981-9646-03-0 024 7 $a10.1007/978-981-96-4603-6 035 $a(CKB)38184759700041 035 $a(DE-He213)978-981-96-4603-6 035 $a(MiAaPQ)EBC31982302 035 $a(Au-PeEL)EBL31982302 035 $a(EXLCZ)9938184759700041 100 $a20250401d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntegrated Uncertainty in Knowledge Modelling and Decision Making $e11th International Symposium, IUKM 2025, Ho Chi Minh City, Vietnam, March 17?19, 2025, Proceedings, Part II /$fedited by Van-Nam Huynh, Katsuhiro Honda, Bac Le, Masahiro Inuiguchi, Hieu T. Huynh 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (XVIII, 335 p. 68 illus., 50 illus. in color.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v15586 311 08$a981-9646-02-2 327 $a -- Uncertainty Management and Decision Making. -- Rough Sets Consisting of Valued Objects in Data Tables with Missing Values. -- Modifying Submodels in Estimation Methods Using Minimum Possible Ranges for Interval Priority Weights under a Crisp Pairwise Comparison Matrix. -- Effective Pair?s Collaboration by Improving Efficiency Intervals. -- All-Time WTA Leaders Ranking Using Incomplete Interval-Valued Fuzzy Preference Relations. -- Distance Between m-Functions in Dempster-Shafer Theory. -- Novel Distances for Fermatean Fuzzy Sets in Multi-Attribute Decision-Making Using Covering-Based Rough Sets. -- What Is Optimal Granularity When Estimating Reliability of a Complex Engineering Systems. -- How Shapley Value and Its Generalizations Can Help in the Analysis of Complex Engineering Systems and What Next. -- Optimization and Statistical Methods. -- A Fuzzy Goal Programming Approach for Production Planning in Packaging Industry. -- Parameter Adjustment for Unbounded Best Case of Interval Linear Program. -- Twin-Based Reinforcement Learning for Solving Multi-Period Portfolio Optimization Problem. -- Application of Modified Rule Table Method to Classification Problems and Comparisons with Neural Network Method. -- Confidence Intervals for Percentile of Inverse Gaussian Distribution with Application to Pm2.5 in Bangkok, Thailand. -- Estimation Methods for the Percentile of Weibull Distribution and Its Application to Wind Speed Data in Southern Thailand. -- Dependence Analysis of Indian Energy Sector Using Regular Vine Copula and Information Transfer Entropy. -- Weight Matrix Variation and Performance Improvement of Regression Analysis. -- Applications. -- Earnings Management, Investment Efficiency, and High Information Uncertainty: Evidence from Vietnam. -- An Integration of OEE and OLE Based on Total Productive Maintenance to Reduce System Losses in Semi-Automated Production. -- The Differential Impacts of Climate Change and Renewable Energy on Income Inequality: A Panel Lasso Analysis of High-Income and Middle-Income Countries. -- Assessing the Financial Impact of Natural Disasters on Global Stock Markets: Evidence from the Five Large Economies. -- Optimizing Public Health: A Nationwide Vaccine Distribution Management System. -- Developing a Customer Churn Prediction Framework Using LFMCP Value Segmentation: Insights from the Thai Insurance Sector. -- The Relationship among Foreign Aid, Poverty Reduction and Economic Growth in CLMV. -- A Method for Classifying Fabricated Responses due to Psychological Biases Using Brain Activity Networks Based on Granger Causality in EEG Responses to the NEO-FFI. -- Clustering Corporate Risk Descriptions through Exploring their Correlations with Management Indicators. -- Data-Driven Validation of Self-Assessment and Peer Reviews in Team-Based Application Development Projects. -- Comparative Effectiveness of Carbon Pricing and Green Finance in Promoting Carbon Mitigation in the Eurozone. -- Impacting Factors on Social Entrepreneurship Education in Universities: Evidence from Vietnam. 330 $a . 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v15586 606 $aArtificial intelligence 606 $aComputer science$xMathematics 606 $aDatabase management 606 $aAlgorithms 606 $aArtificial Intelligence 606 $aMathematics of Computing 606 $aDatabase Management System 606 $aDesign and Analysis of Algorithms 615 0$aArtificial intelligence. 615 0$aComputer science$xMathematics. 615 0$aDatabase management. 615 0$aAlgorithms. 615 14$aArtificial Intelligence. 615 24$aMathematics of Computing. 615 24$aDatabase Management System. 615 24$aDesign and Analysis of Algorithms. 676 $a006.3 702 $aHuynh$b Van-Nam$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHonda$b Katsuhiro$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLe$b Bac$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aInuiguchi$b Masahiro$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHuynh$b Hieu T$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996655267303316 996 $aIntegrated Uncertainty in Knowledge Modelling and Decision Making$94343540 997 $aUNISA