| Description |
1 online resource (xix, 335 pages) : illustrations |
| Series |
World Scientific series in robotics and intelligent systems ; vol. 27 |
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World Scientific series in robotics and intelligent systems ; vol. 27.
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| Contents |
1. Introduction. 1.1. Control systems. 1.2. Advanced control systems. 1.3. Fuzzy control and decision making. 1.4. Chapter outline -- 2. Fuzzy decision making. 2.1. Classification of decision making methods. 2.2. General formulation of decision making. 2.3. Fuzzy decisions. 2.4. Fuzzy multi attribute decision making. 2.5. Summary and concluding remarks -- 3. Fuzzy decision functions. 3.1. Main types of aggregation. 3.2. Triangular norms and conorms. 3.3. Averaging and compensatory operators. 3.4. Generalized operators. 3.5. Weighted aggregation. 3.6. Summary and concluding remarks -- 4. Fuzzy aggregated membership control. 4.1. Decision making and control. 4.2. Conventional fuzzy controllers. 4.3. Nonlinear controllers using decision functions. 4.4. Examples of fuzzy aggregated membership control. 4.5. Summary and concluding remarks -- 5. Modeling and identification. 5.1. Formulation of the modeling problem. 5.2. Fuzzy modeling. 5.3. Fuzzy identification. 5.4. Identification by product-space fuzzy clustering. 5.5. Summary and concluding remarks -- 6. Fuzzy decision making for modeling. 6.1. Fuzzy decisions in fuzzy modeling. 6.2. Defuzzification as a fuzzy decision. 6.3. Application example: fuzzy security assessment. 6.4. Summary and concluding remarks -- 7. Fuzzy model- based control. 7.1. Inversion of fuzzy models. 7.2. Inversion of a singleton fuzzy model. 7.3. Inversion of an affine Takagi-Sugeno fuzzy model. 7.4. On-line adaptation of feed forward fuzzy models. 7.5. Predictive control using the inversion of a fuzzy model. 7.6. Pressure control of a fermentation tank. 7.7. Fuzzy compensation of steady-state errors. 7.8. Summary and concluding remarks -- 8. Performance criteria. 8.1. Design specifications. 8.2. Classical performance specifications. 8.3. Classical performance criteria. 8.4. Fuzzy performance criteria. 8.5. Summary and concluding remarks |
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9. Model-based control with fuzzy decision functions. 9.1. Fuzzy decision making in predictive control. 9.2. Fuzzy model-based predictive control. 9.3. Fuzzy criteria for decision making in control. 9.4. Application examples. 9.5. Design of decision functions from expert knowledge. 9.6. Summary and concluding remarks -- 10. Derivative-free optimization. 10.1. Branch-and-bound optimization for predictive control. 10.2. Branch-and-bound optimization for fuzzy predictive control. 10.3. Application example for fuzzy branch-and-bound. 10.4. Genetic algorithms for optimization in predictive control. 10.5. Application example with genetic algorithms. 10.6. Summary and concluding remarks -- 11. Advanced optimization issues. 11.1. Convex optimization in fuzzy predictive control. 11.2. Application example with convex fuzzy optimization. 11.3. Fuzzy predictive filters. 11.4. Application example for fuzzy predictive filters. 11.5. Summary and concluding remarks -- 12. Application example. 12.1. Air-conditioning systems. 12.2. Fan-coil systems. 12.3. Fuzzy models of the air-conditioning system. 12.4. Controllers applied to the air-conditioning system. 12.5. Summary and concluding remarks -- 13. Future developments. 13.1. Theoretical analysis of FAME controllers. 13.2. Decision support for fuzzy modeling. 13.3. Cooperative control systems. 13.4. Control with approximate models. 13.5. Relation to robust control. 13.6. Hierarchical fuzzy goals in control applications. 13.7. B&B for MIMO systems |
| Summary |
Decision making and control are two fields with distinct methods for solving problems, and yet they are closely related. This book bridges the gap between decision making and control in the field of fuzzy decisions and fuzzy control, and discusses various ways in which fuzzy decision making methods can be applied to systems modeling and control. Fuzzy decision making is a powerful paradigm for dealing with human expert knowledge when one is designing fuzzy model-based controllers. The combination of fuzzy decision making and fuzzy control in this book can lead to novel control schemes that improve the existing controllers in various ways. The following applications of fuzzy decision making methods for designing control systems are considered: Fuzzy decision making for enhancing fuzzy modeling. The values of important parameters in fuzzy modeling algorithms are selected by using fuzzy decision making; Fuzzy decision making for designing signal-based fuzzy controllers. The controller mappings and the defuzzification steps can be obtained by decision making methods; Fuzzy design and performance specifications in model-based control. Fuzzy constraints and fuzzy goals are used; Design of model-based controllers combined with fuzzy decision modules. Human operator experience is incorporated for the performance specification in model-based control.The advantages of bringing together fuzzy control and fuzzy decision making are shown with multiple examples from real and simulated control systems |
| Bibliography |
Includes bibliographical references (pages 319-330) and index |
| Notes |
Print version record |
| Subject |
Fuzzy decision making.
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Decision making.
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Control theory.
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decision making.
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COMPUTERS -- Cybernetics.
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Control theory
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Decision making
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Fuzzy decision making
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Besliskunde.
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Fuzzy sets.
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Controlesystemen.
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| Form |
Electronic book
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| Author |
Kaymak, Uzay
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| ISBN |
9789812777911 |
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9812777911 |
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