This book aims to introduce the state-of-the-art research of stability/performance analysis and optimal synthesis methods for fuzzy-model-based systems. A series of problems are solved with new approaches of design, analysis and synthesis of fuzzy systems, including stabilization control and stabili
Intelligent Control, Filtering and Model Reduction Analysis for Fuzzy-Model-Based Systems (Studies in Systems, Decision and Control, 385)
β Scribed by Xiaojie Su, Yao Wen, Yue Yang, Peng Shi
- Publisher
- Springer
- Year
- 2021
- Tongue
- English
- Leaves
- 322
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
This book aims to introduce the state-of-the-art research of stability/performance analysis and optimal synthesis methods for fuzzy-model-based systems. A series of problems are solved with new approaches of design, analysis and synthesis of fuzzy systems, including stabilization control and stability analysis, dynamic output feedback control, fault detection filter design, and reduced-order model approximation. Some efficient techniques, such as Lyapunov stability theory, linear matrix inequality, reciprocally convex approach, and cone complementary linearization method, are utilized in the approaches. This book is a comprehensive reference for researchers and practitioners working on intelligent control, model reduction, and fault detection of fuzzy systems, and is also a useful source of information for senior undergraduates and graduates in these areas. The readers will benefit from some new concepts and methodologies with theoretical and practical significance in system analysis and control synthesis.
β¦ Table of Contents
Preface
Acknowledgements
Contents
Notations andΒ Acronyms
List ofΒ Figures
List ofΒ Tables
1 Introduction
1.1 Background
1.2 Fuzzy-Model-Based Systems
1.2.1 T-S Fuzzy Dynamic Model
1.2.2 Fuzzy-Model-Based Control System
1.2.3 Stability Analysis of Fuzzy Control Systems
1.3 Intelligent Control of Nonlinear Systems
1.3.1 Intelligent Control
1.3.2 Fuzzy Control
1.4 Reduced-Order Method Synthesis
1.4.1 Model Reduction
1.4.2 Reduced Filtering and Control
1.5 Event-Triggered Strategy
1.6 Publication Contribution
1.7 Publication Outline
Part I Stability Analysis and Fuzzy Control
2 Stabilization Synthesis of T-S Fuzzy Delayed Systems
2.1 Introduction
2.2 System Description and Preliminaries
2.3 Main Results
2.3.1 Stability Analysis
2.3.2 State Feedback Fuzzy Control
2.4 Illustrative Example
2.5 Conclusion
3 Output Feedback Control of Fuzzy Stochastic Systems
3.1 Introduction
3.2 System Description and Preliminaries
3.3 Main Results
3.3.1 State-Feedback Control
3.3.2 Hankel-Norm Output Feedback Control
3.4 Illustrative Example
3.5 Conclusion
4 mathcalL2βmathcalLinfty Output Feedback Control of Fuzzy Switching Systems
4.1 Introduction
4.2 System Description and Preliminaries
4.3 System Performance Analysis
4.4 Dynamic Output Feedback Control
4.4.1 Reduced-Order Controller Design
4.4.2 Full-Order Controller Design
4.5 Illustrative Example
4.6 Conclusion
Part II Fuzzy Filtering and Fault Detection
5 Dissipative Filtering of Fuzzy Switched Systems
5.1 Introduction
5.2 System Description and Preliminaries
5.2.1 System Description
5.2.2 Dissipativity Definition
5.3 Main Results
5.3.1 Dissipativity Performance Analysis
5.3.2 Dissipativity-Based Filter Design
5.4 Illustrative Example
5.5 Conclusion
6 Fault Detection for Switched Stochastic Systems
6.1 Introduction
6.2 System Description and Preliminaries
6.3 Main Results
6.3.1 System Performance Analysis
6.3.2 Fault Detection Filter Design
6.4 Illustrative Example
6.5 Conclusion
7 Reliable Filtering for T-S Fuzzy Time-Delay Systems
7.1 Introduction
7.2 System Description and Preliminaries
7.2.1 System Description
7.2.2 Dissipativity Definition
7.2.3 Reciprocally Convex Approach
7.3 Main Results
7.3.1 Reliable Dissipativity Analysis
7.3.2 Reliable Filter Design with Dissipativity
7.4 Illustrative Example
7.5 Conclusion
Part III Model Reduction and Reduced-Order Synthesis
8 Reduced-Order Model Approximation of Switched Systems
8.1 Introduction
8.2 System Description and Preliminaries
8.3 Main Results
8.3.1 Pre-specified Performance Analysis
8.3.2 Model Approximation by Projection Technique
8.4 Illustrative Example
8.5 Conclusion
9 Model Reduction of Time-Varying Delay Fuzzy Systems
9.1 Introduction
9.2 System Description and Preliminaries
9.3 Main Results
9.3.1 Performance Analysis via Reciprocally Convex Technique
9.3.2 Model Approximation via Projection Technique
9.4 Illustrative Example
9.5 Conclusion
10 Model Approximation of Fuzzy Switched Systems
10.1 Introduction
10.2 System Description and Preliminaries
10.3 Main Results
10.3.1 Hankel-Norm Performance Analysis
10.3.2 Model Approximation by the Hankel-Norm Approach
10.4 Illustrative Example
10.5 Conclusion
11 Reduced-Order Filter Design of Fuzzy Stochastic Systems
11.1 Introduction
11.2 System Description and Preliminaries
11.3 Main Results
11.3.1 mathcalHinfty Performance Analysis
11.3.2 Reduced-Order Filter Design
11.4 Illustrative Example
11.5 Conclusion
Part IV Event-Triggered Fuzzy Control Application
12 Dissipative Event-Triggered Fuzzy Control of Truck-Trailer Systems
12.1 Introduction
12.2 System Description and Preliminaries
12.2.1 Truck-Trailer Model
12.2.2 T-S Fuzzy Systems
12.3 Main Results
12.3.1 Dissipative Performance Analysis
12.3.2 Fuzzy Controller Design
12.4 Simulation Results
12.5 Conclusion
13 Event-Triggered Fuzzy Control of Inverted Pendulum Systems
13.1 Introduction
13.2 System Description and Preliminaries
13.2.1 Inverted Pendulum System
13.2.2 T-S Fuzzy System
13.3 Fuzzy Controller Design
13.3.1 Stability of the Nonlinear Inverted Pendulum Systems
13.3.2 Fuzzy Control of Inverted Pendulum Systems
13.3.3 Event-Triggered Fuzzy Control
13.4 Simulation Results
13.5 Conclusion
14 Conclusion and Further Work
14.1 Conclusion
14.2 Further Work
Appendix References
π SIMILAR VOLUMES
<span>This book highlights original approaches of modelling and intelligent control of cyber-physical systems covering both theoretical and practical aspects. The novel contribution of the book covers the transformation of scientific research and their results into applications for cyber-physical sy
<p><span>This book addresses some of the challenges suffered by the well-known and robust sliding-mode control paradigm. The authors show how the fusion of fuzzy systems with sliding-mode controllers can alleviate some of these problems and promote applicability.</span></p><p><span>Fuzzy systems use
<span>Structural Equation Modeling provides a conceptual and mathematical understanding of structural equation modelling, helping readers across disciplines understand how to test or validate theoretical models, and build relationships between observed variables. In addition to a providing a backgro
<span>This book is devoted to intelligent models and algorithms as the core components of cyber-physical systems. The complexity of cyber-physical systems developing and deploying requires new approaches to its modelling and design. Presents results in the field of modelling technologies that levera
<P>Fuzzy logic control (FLC) has proven to be a popular control methodology for many complex systems in industry, and is often used with great success as an alternative to conventional control techniques. However, because it is fundamentally model free, conventional FLC suffers from a lack of tools