Monitoring and Control of Information-Poor Systems: An Approach based on Fuzzy Relational Models
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品牌: Arthur L. Dexter
基本信息出版社:Wiley; 2 (2012年4月24日)精装:336页正文语种:英语ISBN:0470688696条形码:9780470688694商品尺寸:16.8 x 2.1 x 24.4 cm商品重量:653 gASIN:0470688696商品描述内容简介The monitoring and control of a system whose behaviour is highly uncertain is an important and challenging practical problem. Methods of solution based on fuzzy techniques have generated considerable interest, but very little of the existing literature considers explicit ways of taking uncertainties into account. This book describes an approach to the monitoring and control of information-poor systems that is based on fuzzy relational models which generate fuzzy outputs.The first part ofMonitoring and Control of Information-Poor Systemsaims to clarify why design decisions must take account of the uncertainty associated with optimal choices, and to explain how a fuzzy relational model can be used to generate a fuzzy output, which reflects the uncertainties associated with its predictions. Part two gives a brief introduction to fuzzy decision-making and shows how it can be used to design a predictive control scheme that is suitable for controlling information-poor systems using inaccurate measurements. Part three describes different ways in which fuzzy relational models can be generated online and explains the practical issues associated with their identification and application. The final part of the book provides examples of the use of the previously described techniques in real applications.Key features:Describes techniques applicable to a wide range of engineering, environmental, medical, financial and economic applicationsUses simple examples to help explain the basic techniques for dealing with uncertaintyDescribes a novel design approach based on the use of fuzzy relational modelsConsiders practical issues associated with applying the techniques to real systemsMonitoring and Control of Information-Poor Systemsforms an invaluable resource for a wide range of graduate students, and is also a comprehensive reference for researchers and practitioners working on problems involving mathematical modelling and control.目录Preface xiAbout the Author xvAcknowledgements xviiI ANALYSING THE BEHAVIOUR OF INFORMATION-POOR SYSTEMS1 Characteristics of Information-Poor Systems 31.1 Introduction to Information-Poor Systems 31.1.1 Blast Furnaces31.1.2 Container Cranes31.1.3 Cooperative Control Systems41.1.4 Distillation Columns41.1.5 Drug Administration41.1.6 Electrical Power Generation and Distribution41.1.7 Environmental Risk Assessment Systems41.1.8 Financial Investment and Portfolio Selection51.1.9 Health Care Systems51.1.10 Indoor Climate Control51.1.11 NOx Emissions from Gas Turbines and Internal Combustion Engines61.1.12 Penicillin Production Plant61.1.13 Polymerization Reactors61.1.14 Rotary Kilns61.1.15 Solar Power Plant71.1.16 Wastewater Treatment Plant71.1.17 Wood Pulp Production Plant71.2 Main Causes of Uncertainty 71.2.1 Sources of Modelling Errors81.2.2 Sources of Measurement Errors81.2.3 Reasons for Poorly Defined Objectives and Constraints91.3 Design in the Face of Uncertainty 9References 92 Describing and Propagating Uncertainty 132.1 Methods of Describing Uncertainty 132.1.1 Uncertainty Intervals and Probability Distributions132.1.2 Fuzzy Sets and Fuzzy Numbers142.2 Methods of Propagating Uncertainty 152.2.1 Interval Arithmetic152.2.2 Statistical Methods162.2.3 Monte Carlo Methods162.2.4 Fuzzy Arithmetic172.3 Fuzzy Arithmetic Usingα-Cut Sets and Interval Arithmetic 182.4 Fuzzy Arithmetic Based on the Extension Principle 212.5 Representing and Propagating Uncertainty Using Pseudo-Triangular Membership Functions 242.6 Summary 27References 273 Accounting for Measurement Uncertainty 293.1 Measurement Errors 293.2 Introduction to Fuzzy Random Variables 293.2.1 Definition of a Fuzzy Random Variable303.2.2 Generating Fuzzy Random Variables from a Knowledge of the Random and Systematic Errors303.3 A Hybrid Approach to the Propagation of Uncertainty 323.4 Fuzzy Sensor Fusion Based on the Extension Principle 343.5 Fuzzy Sensors 383.6 Summary 39References 394 Accounting for Modelling Errors in Fuzzy Models 414.1 An Introduction to Rule-Based Models 414.2 Linguistic Fuzzy Models 414.2.1 Fuzzy Rules414.2.2 Fuzzy Inferencing424.2.3 Compositional Rules of Inference434.3 Functional Fuzzy Models 474.4 Fuzzy Neural Networks 484.5 Methods of Generating Fuzzy Models 504.5.1 Modifying Expert Rules to Take Account of Uncertainty504.5.2 Identifying Fuzzy Rules from Data564.6 Defuzzification 584.7 Summary 60References 605 Fuzzy Relational Models 635.1 Introduction to Fuzzy Relations and Fuzzy Relational Models 635.2 Fuzzy FRMs 655.3 Methods of Estimating Rule Confidences from Data 675.4 Estimating Probability Density Functions from Data 705.4.1 Probabilistic Interpretation of RSK Fuzzy Identification715.4.2 Effect of Structural Errors on the Output of a Fuzzy FRM785.4.3 Estimation Based on Limited Amounts of Training Data835.5 Generic Fuzzy Models 865.5.1 Identification of Generic Fuzzy Models875.5.2 Reducing the Time Required to Generate the Training Data915.6 Summary 92References 92II CONTROL OF INFORMATION-POOR SYSTEMS6 Fuzzy Decision-Making 976.1 Risk Assessment in Information-Poor Systems 976.2 Fuzzy Optimization in Information-Poor Systems 996.2.1 Fuzzy Goals and Fuzzy Constraints996.2.2 Fuzzy Aggregation Operators996.2.3 Fuzzy Ranking1006.3 Multi-Stage Decision-Making 1016.3.1 Fuzzy Dynamic Programming1026.3.2 Branch and Bound1036.3.3 Genetic Algorithms1066.4 Fuzzy Decision-Making Based on Intuitionistic Fuzzy Sets 1066.4.1 Definition of an Intuitionistic Fuzzy Set1066.4.2 Multi-Attribute Decision-Making Using Intuitionistic Fuzzy Numbers1076.5 Summary 108References 1087 Predictive Control in Uncertain Systems 1117.1 Model-Based Predictive Control 1117.2 Fuzzy Approaches to Model-Based Control of Uncertain Systems 1127.2.1 Inverse Control of Fuzzy Interval Systems1127.2.2 Fuzzy Model-Based Predictive Control1147.3 Practical Issues Associated with Multi-Step Fuzzy Decision-Making 1157.3.1 Limiting the Accumulation of Uncertainty1157.3.2 Avoiding Excessive Computational Demands When Using Enumerative Search Optimization1157.3.3 Avoiding Excessive Computational Demands When Using Evolutionary Algorithms1167.3.4 Handling Infeasibility1177.3.5 Choosing the Weighting in Multi-Criteria Cost Functions1177.3.6 Dealing with Hard Constraints1187.4 A Simplified Approach to Fuzzy FRM-Based Predictive Control 1187.4.1 The Fuzzy Decision-Maker1197.4.2 Conditional Defuzzification1207.5 FMPC of an Uncertain Dynamic System Based on a Generic Fuzzy FRM 1227.6 Summary 127References 1288 Incorporating Fuzzy Inputs 1298.1 Fuzzy Setpoints and Fuzzy Measurements 1298.1.1 Fuzzy Setpoints1298.1.2 Fuzzy Measurements1298.2 Fuzzy Measures of the Tracking Error and its Derivative 1318.3 Inference with Fuzzy Inputs 1368.4 Fuzzy Output Neural Networks 1388.5 Modelling Input Uncertainty Using a Fuzzy FRM 1408.6 Summary 151References 1519 Disturbance Rejection in Information-Poor Systems 1539.1 Rejecting Unmeasured Disturbances in Uncertain Systems 1549.1.1 Robust Fuzzy Control1549.1.2 Feedback Linearization Using a Fuzzy Disturbance Observer1559.1.3 Fuzzy Model-Based Internal Model Control1559.2 Fuzzy IMC Based on a Fuzzy Output FRM 1579.3 Rejecting Measured Disturbances in Non-Linear Uncertain Systems 1619.4 Fuzzy MPC with Feedforward 1629.5 Summary 166References 166III ONLINE LEARNING IN INFORMATION-POOR SYSTEMS10 Online Model Identification in Information-Poor Environments 17110.1 Online Fuzzy Identification Schemes 17110.1.1 Recursive Fuzzy Least-Squares17110.1.2 Recursive Forms of the RSK Algorithm17210.2 Effect of Poor-Quality and Incomplete Training Data 17610.3 Ways of Reducing the Computational Demands 17710.3.1 Evolving Fuzzy Models17710.3.2 Hierarchical Fuzzy Models18110.4 Summary 185References 18511 Adaptive Model-Based Control of Information-Poor Systems 18711.1 Robust Adaptive Fuzzy Control 18711.2 Adaptive Fuzzy FRM-Based Predictive Control 18811.3 Commissioning the Controller 18911.3.1 Methods of Incorporating Prior Knowledge18911.3.2 Initialization Using a Generic Fuzzy FRM18911.4 Generating an Optimal Control Signal Using a Partially Trained Model 19211.4.1 Taking the Amount of Training into Account19211.4.2 Incorporating a Secondary Controller19411.4.3 Combining the Fuzzy Predictions Generated by More than One Model20111.5 Dealing with the Effects of Disturbances 20211.5.1 Adaptive Feedforward Control Based on an Inaccurate Disturbance Measurement20311.6 Summary 209References 20912 Adaptive Model-Free Control of Information-Poor Systems 21112.1 Introduction to Model-Free Adaptive Control of Non-Linear Systems 21112.2 Fuzzy FRM-Based Direct Adaptive Control 21112.3 Behaviour in the Presence of a Noisy Measurement of the Plant Output 21312.4 Behaviour in the Presence of an Unmeasured Disturbance 21812.5 Accounting for Uncertainty Arising from a Measured Disturbance 22212.6 Summary 227References 22713 Fault Diagnosis in Information-Poor Systems 22913.1 Introduction to Fault Detection and Isolation in Non-Linear Uncertain Systems 22913.1.1 Model-Based Methods for Non-Linear Systems23013.1.2 Ways of Accounting for Uncertainty23213.2 A Fuzzy FRM-Based Fault Diagnosis Scheme 23313.2.1 Measuring the Similarity of FRMs23413.2.2 Accumulating Evidence of Fault-Free or Faulty Operation23613.2.3 Generating Robust Generic Models of Faulty Operation23913.2.4 Multi-Step Fault Diagnosis23913.3 Summary 242References 243IV SOME EXAMPLE APPLICATIONS14 Control of Thermal Comfort 24714.1 Main Sources of Uncertainty and Practical Considerations 24814.2 Review of Approaches Suggested for Dealing with the Uncertainty 24914.3 Design of the Fuzzy FRM-Based Control System 24914.3.1 The Fuzzy FRM25014.3.2 The Fuzzy Cost Functions25214.3.3 The Fuzzy Goals25214.3.4 The Fuzzy Decision-Maker25414.3.5 The Conditional Defuzzifier25414.4 Performance of the Thermal Comfort Controller 25414.5 Concluding Remarks 258References 25915 Identification of Faults in Air-Conditioning Systems 26115.1 Main Sources of Uncertainty and Practical Considerations 26115.2 Design of a Fuzzy FRM-Based Monitoring System for a Cooling Coil Subsystem 26315.3 Diagnosis of Known Faults in a Simulated Cooling Coil Subsystem 26415.3.1 Fault-Free Operation26415.3.2 Leaky Valve26415.3.3 Fouled Coil26515.3.4 Valve Stuck in the Fully Closed Position26615.3.5 Valve Stuck in the Midway Position26715.3.6 Valve Stuck in the Fully Open Position26815.4 Commissioning of Air-Handling Units 26915.5 Concluding Remarks 272References 27216 Control of Heat Exchangers 27516.1 Main Sources of Uncertainty and Practical Considerations 27516.2 Design of a Fuzzy FRM-Based Predictive Controller 27616.3 Design of a Fuzzy FRM-Based Internal Model Control Scheme 28316.4 Concluding Remarks 290Referenc...
