Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach (Hardcover)

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 for systematic stability analysis and controller design. To address this problem, many model-based fuzzy control approaches have been developed, with the fuzzy dynamic model or the Takagi and Sugeno (T–S) fuzzy model-based approaches receiving the greatest attention.

Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach offers a unique reference devoted to the systematic analysis and synthesis of model-based fuzzy control systems. After giving a brief review of the varieties of FLC, including the T–S fuzzy model-based control, it fully explains the fundamental concepts of fuzzy sets, fuzzy logic, and fuzzy systems. This enables the book to be self-contained and provides a basis for later chapters, which cover:

• T–S fuzzy modeling and identification via nonlinear models or data
• Stability analysis of T–S fuzzy systems
• Stabilization controller synthesis as well as robust H∞ and observer and output feedback controller synthesis
• Robust controller synthesis of uncertain T–S fuzzy systems
• Time-delay T–S fuzzy systems
• Fuzzy model predictive control
• Robust fuzzy filtering
• Adaptive control of T–S fuzzy systems

A reference for scientists and engineers in systems and control, the book also serves the needs of graduate students exploring fuzzy logic control. It readily demonstrates that conventional control technology and fuzzy logic control can be elegantly combined and further developed so that disadvantages of conventional FLC can be avoided and the horizon of conventional control technology greatly extended. Many chapters feature application simulation examples and practical numerical examples based on MATLAB^®.

模糊邏輯控制（FLC）已被證明是工業中許多複雜系統的流行控制方法，通常作為傳統控制技術的替代方案而取得巨大成功。然而，由於它基本上是無模型的，傳統FLC在系統穩定性分析和控制器設計方面缺乏工具。為了解決這個問題，許多基於模型的模糊控制方法已經被開發出來，其中模糊動態模型或Takagi和Sugeno（T-S）模糊模型為主要研究方向。

《模糊控制系統的分析與合成：基於模型的方法》是一本專注於基於模型的模糊控制系統的系統性分析和合成的獨特參考書。在簡要回顧FLC的各種變體（包括T-S模糊模型控制）之後，它完整解釋了模糊集合、模糊邏輯和模糊系統的基本概念。這使得本書具有自包含性，並為後續章節提供了基礎，這些章節包括：

- 通過非線性模型或數據進行T-S模糊建模和識別
- T-S模糊系統的穩定性分析
- 穩定化控制器的合成以及強健H∞、觀測器和輸出反饋控制器的合成
- 不確定T-S模糊系統的強健控制器合成
- 延遲時間的T-S模糊系統
- 模糊模型預測控制
- 強健模糊濾波
- T-S模糊系統的自適應控制

本書是系統和控制領域的科學家和工程師的參考書，同時也滿足了探索模糊邏輯控制的研究生的需求。它清楚地展示了傳統控制技術和模糊邏輯控制可以優雅地結合和進一步發展，從而避免了傳統FLC的缺點，並大大擴展了傳統控制技術的應用範圍。許多章節都包含基於MATLAB®的應用仿真示例和實際數值示例。