book

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

by Arthur L. Dexter

April 2012

Intermediate to advanced

336 pages

9h 25m

English

Wiley

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1.1 Introduction to Information-Poor Systems1.2 Main Causes of Uncertainty1.3 Design in the Face of Uncertainty
2.1 Methods of Describing Uncertainty2.2 Methods of Propagating Uncertainty2.3 Fuzzy Arithmetic Using α-Cut Sets and Interval Arithmetic2.4 Fuzzy Arithmetic Based on the Extension Principle2.5 Representing and Propagating Uncertainty Using Pseudo-Triangular Membership Functions2.6 Summary
3.1 Measurement Errors3.2 Introduction to Fuzzy Random Variables3.3 A Hybrid Approach to the Propagation of Uncertainty3.4 Fuzzy Sensor Fusion Based on the Extension Principle3.5 Fuzzy Sensors3.6 Summary

4.1 An Introduction to Rule-Based Models4.2 Linguistic Fuzzy Models4.3 Functional Fuzzy Models4.4 Fuzzy Neural Networks4.5 Methods of Generating Fuzzy Models4.6 Defuzzification4.7 Summary
5.1 Introduction to Fuzzy Relations and Fuzzy Relational Models5.2 Fuzzy FRMs5.3 Methods of Estimating Rule Confidences from Data5.4 Estimating Probability Density Functions from Data5.5 Generic Fuzzy Models5.6 Summary
6.1 Risk Assessment in Information-Poor Systems6.2 Fuzzy Optimization in Information-Poor Systems6.3 Multi-Stage Decision-Making6.4 Fuzzy Decision-Making Based on Intuitionistic Fuzzy Sets6.5 Summary
7.1 Model-Based Predictive Control7.2 Fuzzy Approaches to Model-Based Control of Uncertain Systems7.3 Practical Issues Associated with Multi-Step Fuzzy Decision-Making7.4 A Simplified Approach to Fuzzy FRM-Based Predictive Control7.5 FMPC of an Uncertain Dynamic System Based on a Generic Fuzzy FRM7.6 Summary
8.1 Fuzzy Setpoints and Fuzzy Measurements8.2 Fuzzy Measures of the Tracking Error and its Derivative8.3 Inference with Fuzzy Inputs8.4 Fuzzy Output Neural Networks8.5 Modelling Input Uncertainty Using a Fuzzy FRM8.6 Summary
9.1 Rejecting Unmeasured Disturbances in Uncertain Systems9.2 Fuzzy IMC Based on a Fuzzy Output FRM9.3 Rejecting Measured Disturbances in Non-Linear Uncertain Systems9.4 Fuzzy MPC with Feedforward9.5 Summary
10.1 Online Fuzzy Identification Schemes10.2 Effect of Poor-Quality and Incomplete Training Data10.3 Ways of Reducing the Computational Demands10.4 Summary
11.1 Robust Adaptive Fuzzy Control11.2 Adaptive Fuzzy FRM-Based Predictive Control11.3 Commissioning the Controller11.4 Generating an Optimal Control Signal Using a Partially Trained Model11.5 Dealing with the Effects of Disturbances11.6 Summary
12.1 Introduction to Model-Free Adaptive Control of Non-Linear Systems12.2 Fuzzy FRM-Based Direct Adaptive Control12.3 Behaviour in the Presence of a Noisy Measurement of the Plant Output12.4 Behaviour in the Presence of an Unmeasured Disturbance12.5 Accounting for Uncertainty Arising from a Measured Disturbance12.6 Summary
13.1 Introduction to Fault Detection and Isolation in Non-Linear Uncertain Systems13.2 A Fuzzy FRM-Based Fault Diagnosis Scheme13.3 Summary
14.1 Main Sources of Uncertainty and Practical Considerations14.2 Review of Approaches Suggested for Dealing with the Uncertainty14.3 Design of the Fuzzy FRM-Based Control System14.4 Performance of the Thermal Comfort Controller14.5 Concluding Remarks
15.1 Main Sources of Uncertainty and Practical Considerations15.2 Design of a Fuzzy FRM-Based Monitoring System for a Cooling Coil Subsystem15.3 Diagnosis of Known Faults in a Simulated Cooling Coil Subsystem15.4 Commissioning of Air-Handling Units15.5 Concluding Remarks
16.1 Main Sources of Uncertainty and Practical Considerations16.2 Design of a Fuzzy FRM-Based Predictive Controller16.3 Design of a Fuzzy FRM-Based Internal Model Control Scheme16.4 Concluding Remarks
17.1 Review of Approaches Suggested for Dealing with Sensor Bias17.2 An Example Application17.3 Using Bias Estimation and Fuzzy Data Fusion to Improve Automated Commissioning in Air-Handling Units17.4 Concluding Remarks