New approaches to fuzzy modeling and control :: design and analysis /
Fuzzy logic has found applications in an incredibly wide range of areas in the relatively short time since its conception. It was invented by Lotfi Zadeh, a leading systems expert, so it is perhaps not surprising that system theory is one of the areas in which fuzzy logic has made a profound impact....
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore ; River Edge, NJ :
World Scientific,
©2000.
|
| Schriftenreihe: | Series in machine perception and artificial intelligence ;
vol. 38. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Fuzzy logic has found applications in an incredibly wide range of areas in the relatively short time since its conception. It was invented by Lotfi Zadeh, a leading systems expert, so it is perhaps not surprising that system theory is one of the areas in which fuzzy logic has made a profound impact. Fuzzy logic combined with the paradigm of computing with words allows the use and manipulation of human knowledge and reasoning in the modeling and control of dynamical systems. This monograph presents new approaches to the construction of fuzzy models and to the design of fuzzy controllers. The emphasis is on developing methods that allow systematic design on the one hand and mathematical analysis of the resulting system on the other. In particular, the methods described allow rigorous analysis of the stability and robustness of the systems, which are crucial issues in control theory. The first theme of the book is a new approach to the systematic design and analysis of fuzzy controllers, given linguistic information concerning the plant and the control objective. The new approach, fuzzy Lyapunov synthesis, is a computing with words version of the well-known (classical) Lyapunov synthesis method. The second theme of the book is to show that fuzzy controllers are in fact solutions of a nonlinear optimal control problem. The authors formulate a novel nonlinear optimal control problem, consisting of a new state-space model - referred to as the hyperbolic state-space model - and a new cost functional, and show that its solution is a fuzzy controller. This leads to a new framework for fuzzy modeling and control that combines the advantages of the fuzzy world, such as linguistic interpretability, and of classical optimal control theory, such as guaranteed stability and robustness. |
| Beschreibung: | 1 online resource (xiii, 185 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 177-181) and index. |
| ISBN: | 9789812792716 9812792716 |
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| 505 | 0 | |a Ch. 1. Prologue. 1.1. Background. 1.2. Preview. 1.3. Notes and references -- ch. 2. Fuzzy Lyapunov synthesis. 2.1. Introduction. 2.2. The design method. 2.3. Putting it to work: Design examples. 2.4. Summary and discussion. 2.5. Notes and references -- ch. 3. Fuzzy Lyapunov synthesis and stability analysis. 3.1. Introduction. 3.2. Classical Lyapunov stability analysis. 3.3. Fuzzy Lyapunov stability analysis. 3.4. Summary and discussion. 3.5. Notes and references -- ch. 4. Adaptive fuzzy controller design. 4.1. Introduction. 4.2. Designing the rule-base. 4.3. Adaptation. 4.4. Example: Inverted pendulum control. 4.5. Summary and discussion. 4.6. Notes and references -- ch. 5. Inverse optimality for fuzzy controllers. 5.1. Introduction. 5.2. Review of optimality and inverse optimality. 5.3. The scalar case. 5.4. N-th order systems. 5.5. Stability. 5.6. Robustness. 5.7. Summary and discussion. 5.8. Notes and references -- ch. 6. Hyperbolic approach to fuzzy modeling. 6.1. Introduction. 6.2. The hyperbolic state-space model. 6.3. Approximation capabilities. 6.4. The modeling process. 6.5. On-line parameter estimation. 6.6. Summary and discussion. 6.7. Notes and references -- ch. 7. Fuzzy controllers for the hyperbolic state-space model. 7.1. Introduction. 7.2. Diagonal stabilization. 7.3. Nonlinear H[symbol] optimal control. 7.4. Nonlinear H[symbol] optimal control. 7.5. Linguistic interpretation. 7.6. Summary and discussion. 7.7. Notes and references -- ch. 8. Epilogue. | |
| 520 | |a Fuzzy logic has found applications in an incredibly wide range of areas in the relatively short time since its conception. It was invented by Lotfi Zadeh, a leading systems expert, so it is perhaps not surprising that system theory is one of the areas in which fuzzy logic has made a profound impact. Fuzzy logic combined with the paradigm of computing with words allows the use and manipulation of human knowledge and reasoning in the modeling and control of dynamical systems. This monograph presents new approaches to the construction of fuzzy models and to the design of fuzzy controllers. The emphasis is on developing methods that allow systematic design on the one hand and mathematical analysis of the resulting system on the other. In particular, the methods described allow rigorous analysis of the stability and robustness of the systems, which are crucial issues in control theory. The first theme of the book is a new approach to the systematic design and analysis of fuzzy controllers, given linguistic information concerning the plant and the control objective. The new approach, fuzzy Lyapunov synthesis, is a computing with words version of the well-known (classical) Lyapunov synthesis method. The second theme of the book is to show that fuzzy controllers are in fact solutions of a nonlinear optimal control problem. The authors formulate a novel nonlinear optimal control problem, consisting of a new state-space model - referred to as the hyperbolic state-space model - and a new cost functional, and show that its solution is a fuzzy controller. This leads to a new framework for fuzzy modeling and control that combines the advantages of the fuzzy world, such as linguistic interpretability, and of classical optimal control theory, such as guaranteed stability and robustness. | ||
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| contents | Ch. 1. Prologue. 1.1. Background. 1.2. Preview. 1.3. Notes and references -- ch. 2. Fuzzy Lyapunov synthesis. 2.1. Introduction. 2.2. The design method. 2.3. Putting it to work: Design examples. 2.4. Summary and discussion. 2.5. Notes and references -- ch. 3. Fuzzy Lyapunov synthesis and stability analysis. 3.1. Introduction. 3.2. Classical Lyapunov stability analysis. 3.3. Fuzzy Lyapunov stability analysis. 3.4. Summary and discussion. 3.5. Notes and references -- ch. 4. Adaptive fuzzy controller design. 4.1. Introduction. 4.2. Designing the rule-base. 4.3. Adaptation. 4.4. Example: Inverted pendulum control. 4.5. Summary and discussion. 4.6. Notes and references -- ch. 5. Inverse optimality for fuzzy controllers. 5.1. Introduction. 5.2. Review of optimality and inverse optimality. 5.3. The scalar case. 5.4. N-th order systems. 5.5. Stability. 5.6. Robustness. 5.7. Summary and discussion. 5.8. Notes and references -- ch. 6. Hyperbolic approach to fuzzy modeling. 6.1. Introduction. 6.2. The hyperbolic state-space model. 6.3. Approximation capabilities. 6.4. The modeling process. 6.5. On-line parameter estimation. 6.6. Summary and discussion. 6.7. Notes and references -- ch. 7. Fuzzy controllers for the hyperbolic state-space model. 7.1. Introduction. 7.2. Diagonal stabilization. 7.3. Nonlinear H[symbol] optimal control. 7.4. Nonlinear H[symbol] optimal control. 7.5. Linguistic interpretation. 7.6. Summary and discussion. 7.7. Notes and references -- ch. 8. Epilogue. |
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| dewey-sort | 3629.836 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Electronic eBook |
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| indexdate | 2024-11-27T13:25:14Z |
| institution | BVB |
| isbn | 9789812792716 9812792716 |
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| series | Series in machine perception and artificial intelligence ; |
| series2 | Series in machine perception and artificial intelligence ; |
| spelling | Margaliot, Michael. New approaches to fuzzy modeling and control : design and analysis / Michael Margaliot, Gideon Langholz. Singapore ; River Edge, NJ : World Scientific, ©2000. 1 online resource (xiii, 185 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Series in machine perception and artificial intelligence ; vol. 38 Includes bibliographical references (pages 177-181) and index. Print version record. Ch. 1. Prologue. 1.1. Background. 1.2. Preview. 1.3. Notes and references -- ch. 2. Fuzzy Lyapunov synthesis. 2.1. Introduction. 2.2. The design method. 2.3. Putting it to work: Design examples. 2.4. Summary and discussion. 2.5. Notes and references -- ch. 3. Fuzzy Lyapunov synthesis and stability analysis. 3.1. Introduction. 3.2. Classical Lyapunov stability analysis. 3.3. Fuzzy Lyapunov stability analysis. 3.4. Summary and discussion. 3.5. Notes and references -- ch. 4. Adaptive fuzzy controller design. 4.1. Introduction. 4.2. Designing the rule-base. 4.3. Adaptation. 4.4. Example: Inverted pendulum control. 4.5. Summary and discussion. 4.6. Notes and references -- ch. 5. Inverse optimality for fuzzy controllers. 5.1. Introduction. 5.2. Review of optimality and inverse optimality. 5.3. The scalar case. 5.4. N-th order systems. 5.5. Stability. 5.6. Robustness. 5.7. Summary and discussion. 5.8. Notes and references -- ch. 6. Hyperbolic approach to fuzzy modeling. 6.1. Introduction. 6.2. The hyperbolic state-space model. 6.3. Approximation capabilities. 6.4. The modeling process. 6.5. On-line parameter estimation. 6.6. Summary and discussion. 6.7. Notes and references -- ch. 7. Fuzzy controllers for the hyperbolic state-space model. 7.1. Introduction. 7.2. Diagonal stabilization. 7.3. Nonlinear H[symbol] optimal control. 7.4. Nonlinear H[symbol] optimal control. 7.5. Linguistic interpretation. 7.6. Summary and discussion. 7.7. Notes and references -- ch. 8. Epilogue. Fuzzy logic has found applications in an incredibly wide range of areas in the relatively short time since its conception. It was invented by Lotfi Zadeh, a leading systems expert, so it is perhaps not surprising that system theory is one of the areas in which fuzzy logic has made a profound impact. Fuzzy logic combined with the paradigm of computing with words allows the use and manipulation of human knowledge and reasoning in the modeling and control of dynamical systems. This monograph presents new approaches to the construction of fuzzy models and to the design of fuzzy controllers. The emphasis is on developing methods that allow systematic design on the one hand and mathematical analysis of the resulting system on the other. In particular, the methods described allow rigorous analysis of the stability and robustness of the systems, which are crucial issues in control theory. The first theme of the book is a new approach to the systematic design and analysis of fuzzy controllers, given linguistic information concerning the plant and the control objective. The new approach, fuzzy Lyapunov synthesis, is a computing with words version of the well-known (classical) Lyapunov synthesis method. The second theme of the book is to show that fuzzy controllers are in fact solutions of a nonlinear optimal control problem. The authors formulate a novel nonlinear optimal control problem, consisting of a new state-space model - referred to as the hyperbolic state-space model - and a new cost functional, and show that its solution is a fuzzy controller. This leads to a new framework for fuzzy modeling and control that combines the advantages of the fuzzy world, such as linguistic interpretability, and of classical optimal control theory, such as guaranteed stability and robustness. Fuzzy systems. http://id.loc.gov/authorities/subjects/sh85052628 Adaptive control systems. http://id.loc.gov/authorities/subjects/sh85000803 Systèmes flous. Systèmes adaptatifs. TECHNOLOGY & ENGINEERING Automation. bisacsh TECHNOLOGY & ENGINEERING Robotics. bisacsh Adaptive control systems fast Fuzzy systems fast FUZZY SYSTEMS. nasat LOGIC PROGRAMMING. nasat ADAPTIVE CONTROL. nasat ARTIFICIAL INTELLIGENCE. nasat LIAPUNOV FUNCTIONS. nasat Systèmes flous. ram Systèmes adaptatifs. ram Langholz, Gideon. Print version: Margaliot, Michael. New approaches to fuzzy modeling and control. Singapore ; River Edge, NJ : World Scientific, ©2000 9810243340 (OCoLC)45254905 Series in machine perception and artificial intelligence ; vol. 38. http://id.loc.gov/authorities/names/n91107585 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=514159 Volltext |
| spellingShingle | Margaliot, Michael New approaches to fuzzy modeling and control : design and analysis / Series in machine perception and artificial intelligence ; Ch. 1. Prologue. 1.1. Background. 1.2. Preview. 1.3. Notes and references -- ch. 2. Fuzzy Lyapunov synthesis. 2.1. Introduction. 2.2. The design method. 2.3. Putting it to work: Design examples. 2.4. Summary and discussion. 2.5. Notes and references -- ch. 3. Fuzzy Lyapunov synthesis and stability analysis. 3.1. Introduction. 3.2. Classical Lyapunov stability analysis. 3.3. Fuzzy Lyapunov stability analysis. 3.4. Summary and discussion. 3.5. Notes and references -- ch. 4. Adaptive fuzzy controller design. 4.1. Introduction. 4.2. Designing the rule-base. 4.3. Adaptation. 4.4. Example: Inverted pendulum control. 4.5. Summary and discussion. 4.6. Notes and references -- ch. 5. Inverse optimality for fuzzy controllers. 5.1. Introduction. 5.2. Review of optimality and inverse optimality. 5.3. The scalar case. 5.4. N-th order systems. 5.5. Stability. 5.6. Robustness. 5.7. Summary and discussion. 5.8. Notes and references -- ch. 6. Hyperbolic approach to fuzzy modeling. 6.1. Introduction. 6.2. The hyperbolic state-space model. 6.3. Approximation capabilities. 6.4. The modeling process. 6.5. On-line parameter estimation. 6.6. Summary and discussion. 6.7. Notes and references -- ch. 7. Fuzzy controllers for the hyperbolic state-space model. 7.1. Introduction. 7.2. Diagonal stabilization. 7.3. Nonlinear H[symbol] optimal control. 7.4. Nonlinear H[symbol] optimal control. 7.5. Linguistic interpretation. 7.6. Summary and discussion. 7.7. Notes and references -- ch. 8. Epilogue. Fuzzy systems. http://id.loc.gov/authorities/subjects/sh85052628 Adaptive control systems. http://id.loc.gov/authorities/subjects/sh85000803 Systèmes flous. Systèmes adaptatifs. TECHNOLOGY & ENGINEERING Automation. bisacsh TECHNOLOGY & ENGINEERING Robotics. bisacsh Adaptive control systems fast Fuzzy systems fast FUZZY SYSTEMS. nasat LOGIC PROGRAMMING. nasat ADAPTIVE CONTROL. nasat ARTIFICIAL INTELLIGENCE. nasat LIAPUNOV FUNCTIONS. nasat Systèmes flous. ram Systèmes adaptatifs. ram |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85052628 http://id.loc.gov/authorities/subjects/sh85000803 |
| title | New approaches to fuzzy modeling and control : design and analysis / |
| title_auth | New approaches to fuzzy modeling and control : design and analysis / |
| title_exact_search | New approaches to fuzzy modeling and control : design and analysis / |
| title_full | New approaches to fuzzy modeling and control : design and analysis / Michael Margaliot, Gideon Langholz. |
| title_fullStr | New approaches to fuzzy modeling and control : design and analysis / Michael Margaliot, Gideon Langholz. |
| title_full_unstemmed | New approaches to fuzzy modeling and control : design and analysis / Michael Margaliot, Gideon Langholz. |
| title_short | New approaches to fuzzy modeling and control : |
| title_sort | new approaches to fuzzy modeling and control design and analysis |
| title_sub | design and analysis / |
| topic | Fuzzy systems. http://id.loc.gov/authorities/subjects/sh85052628 Adaptive control systems. http://id.loc.gov/authorities/subjects/sh85000803 Systèmes flous. Systèmes adaptatifs. TECHNOLOGY & ENGINEERING Automation. bisacsh TECHNOLOGY & ENGINEERING Robotics. bisacsh Adaptive control systems fast Fuzzy systems fast FUZZY SYSTEMS. nasat LOGIC PROGRAMMING. nasat ADAPTIVE CONTROL. nasat ARTIFICIAL INTELLIGENCE. nasat LIAPUNOV FUNCTIONS. nasat Systèmes flous. ram Systèmes adaptatifs. ram |
| topic_facet | Fuzzy systems. Adaptive control systems. Systèmes flous. Systèmes adaptatifs. TECHNOLOGY & ENGINEERING Automation. TECHNOLOGY & ENGINEERING Robotics. Adaptive control systems Fuzzy systems FUZZY SYSTEMS. LOGIC PROGRAMMING. ADAPTIVE CONTROL. ARTIFICIAL INTELLIGENCE. LIAPUNOV FUNCTIONS. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=514159 |
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