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Monitoring and Control of Information-Poor Systems: An Approach Based on Fuzzy Relational Models

Book Description

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 of Monitoring and Control of Information-Poor Systems aims 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 applications

  • Uses simple examples to help explain the basic techniques for dealing with uncertainty

  • Describes a novel design approach based on the use of fuzzy relational models

  • Considers practical issues associated with applying the techniques to real systems

Monitoring and Control of Information-Poor Systems forms 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.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Preface
  5. About the Author
  6. Acknowledgments
  7. Part I: Analysing the behaviour of information-poor systems
    1. Chapter 1: Characteristics of Information-Poor Systems
      1. 1.1 Introduction to Information-Poor Systems
      2. 1.2 Main Causes of Uncertainty
      3. 1.3 Design in the Face of Uncertainty
    2. Chapter 2: Describing and Propagating Uncertainty
      1. 2.1 Methods of Describing Uncertainty
      2. 2.2 Methods of Propagating Uncertainty
      3. 2.3 Fuzzy Arithmetic Using α-Cut Sets and Interval Arithmetic
      4. 2.4 Fuzzy Arithmetic Based on the Extension Principle
      5. 2.5 Representing and Propagating Uncertainty Using Pseudo-Triangular Membership Functions
      6. 2.6 Summary
    3. Chapter 3: Accounting for Measurement Uncertainty
      1. 3.1 Measurement Errors
      2. 3.2 Introduction to Fuzzy Random Variables
      3. 3.3 A Hybrid Approach to the Propagation of Uncertainty
      4. 3.4 Fuzzy Sensor Fusion Based on the Extension Principle
      5. 3.5 Fuzzy Sensors
      6. 3.6 Summary
    4. Chapter 4: Accounting for Modelling Errors in Fuzzy Models
      1. 4.1 An Introduction to Rule-Based Models
      2. 4.2 Linguistic Fuzzy Models
      3. 4.3 Functional Fuzzy Models
      4. 4.4 Fuzzy Neural Networks
      5. 4.5 Methods of Generating Fuzzy Models
      6. 4.6 Defuzzification
      7. 4.7 Summary
    5. Chapter 5: Fuzzy Relational Models
      1. 5.1 Introduction to Fuzzy Relations and Fuzzy Relational Models
      2. 5.2 Fuzzy FRMs
      3. 5.3 Methods of Estimating Rule Confidences from Data
      4. 5.4 Estimating Probability Density Functions from Data
      5. 5.5 Generic Fuzzy Models
      6. 5.6 Summary
  8. Part II: Control of information-poor systems
    1. Chapter 6: Fuzzy Decision-Making
      1. 6.1 Risk Assessment in Information-Poor Systems
      2. 6.2 Fuzzy Optimization in Information-Poor Systems
      3. 6.3 Multi-Stage Decision-Making
      4. 6.4 Fuzzy Decision-Making Based on Intuitionistic Fuzzy Sets
      5. 6.5 Summary
    2. Chapter 7: Predictive Control in Uncertain Systems
      1. 7.1 Model-Based Predictive Control
      2. 7.2 Fuzzy Approaches to Model-Based Control of Uncertain Systems
      3. 7.3 Practical Issues Associated with Multi-Step Fuzzy Decision-Making
      4. 7.4 A Simplified Approach to Fuzzy FRM-Based Predictive Control
      5. 7.5 FMPC of an Uncertain Dynamic System Based on a Generic Fuzzy FRM
      6. 7.6 Summary
    3. Chapter 8: Incorporating Fuzzy Inputs
      1. 8.1 Fuzzy Setpoints and Fuzzy Measurements
      2. 8.2 Fuzzy Measures of the Tracking Error and its Derivative
      3. 8.3 Inference with Fuzzy Inputs
      4. 8.4 Fuzzy Output Neural Networks
      5. 8.5 Modelling Input Uncertainty Using a Fuzzy FRM
      6. 8.6 Summary
    4. Chapter 9: Disturbance Rejection in Information-Poor Systems
      1. 9.1 Rejecting Unmeasured Disturbances in Uncertain Systems
      2. 9.2 Fuzzy IMC Based on a Fuzzy Output FRM
      3. 9.3 Rejecting Measured Disturbances in Non-Linear Uncertain Systems
      4. 9.4 Fuzzy MPC with Feedforward
      5. 9.5 Summary
  9. Part III: Online learning in information-poor systems
    1. Chapter 10: Online Model Identification in Information-Poor Environments
      1. 10.1 Online Fuzzy Identification Schemes
      2. 10.2 Effect of Poor-Quality and Incomplete Training Data
      3. 10.3 Ways of Reducing the Computational Demands
      4. 10.4 Summary
    2. Chapter 11: Adaptive Model-Based Control of Information-Poor Systems
      1. 11.1 Robust Adaptive Fuzzy Control
      2. 11.2 Adaptive Fuzzy FRM-Based Predictive Control
      3. 11.3 Commissioning the Controller
      4. 11.4 Generating an Optimal Control Signal Using a Partially Trained Model
      5. 11.5 Dealing with the Effects of Disturbances
      6. 11.6 Summary
    3. Chapter 12: Adaptive Model-Free Control of Information-Poor Systems
      1. 12.1 Introduction to Model-Free Adaptive Control of Non-Linear Systems
      2. 12.2 Fuzzy FRM-Based Direct Adaptive Control
      3. 12.3 Behaviour in the Presence of a Noisy Measurement of the Plant Output
      4. 12.4 Behaviour in the Presence of an Unmeasured Disturbance
      5. 12.5 Accounting for Uncertainty Arising from a Measured Disturbance
      6. 12.6 Summary
    4. Chapter 13: Fault Diagnosis in Information-Poor Systems
      1. 13.1 Introduction to Fault Detection and Isolation in Non-Linear Uncertain Systems
      2. 13.2 A Fuzzy FRM-Based Fault Diagnosis Scheme
      3. 13.3 Summary
  10. Part IV: Some example applications
    1. Chapter 14: Control of Thermal Comfort
      1. 14.1 Main Sources of Uncertainty and Practical Considerations
      2. 14.2 Review of Approaches Suggested for Dealing with the Uncertainty
      3. 14.3 Design of the Fuzzy FRM-Based Control System
      4. 14.4 Performance of the Thermal Comfort Controller
      5. 14.5 Concluding Remarks
    2. Chapter 15: Identification of Faults in Air-Conditioning Systems
      1. 15.1 Main Sources of Uncertainty and Practical Considerations
      2. 15.2 Design of a Fuzzy FRM-Based Monitoring System for a Cooling Coil Subsystem
      3. 15.3 Diagnosis of Known Faults in a Simulated Cooling Coil Subsystem
      4. 15.4 Commissioning of Air-Handling Units
      5. 15.5 Concluding Remarks
    3. Chapter 16: Control of Heat Exchangers
      1. 16.1 Main Sources of Uncertainty and Practical Considerations
      2. 16.2 Design of a Fuzzy FRM-Based Predictive Controller
      3. 16.3 Design of a Fuzzy FRM-Based Internal Model Control Scheme
      4. 16.4 Concluding Remarks
    4. Chapter 17: Measurement of Spatially Distributed Quantities
      1. 17.1 Review of Approaches Suggested for Dealing with Sensor Bias
      2. 17.2 An Example Application
      3. 17.3 Using Bias Estimation and Fuzzy Data Fusion to Improve Automated Commissioning in Air-Handling Units
      4. 17.4 Concluding Remarks
  11. Index