Analysis and Synthesis for Interval Type-2 Fuzzy-Model-Based - download pdf or read online

By Hongyi Li, Ligang Wu, Hak-Keung Lam, Yabin Gao

ISBN-10: 9811005923

ISBN-13: 9789811005923

ISBN-10: 9811005931

ISBN-13: 9789811005930

This booklet develops a suite of reference equipment in a position to modeling uncertainties current in club services, and examining and synthesizing the period type-2 fuzzy structures with wanted performances. It additionally presents quite a few simulation effects for numerous examples, which fill convinced gaps during this quarter of study and will function benchmark strategies for the readers.
Interval type-2 T-S fuzzy versions offer a handy and versatile approach for research and synthesis of advanced nonlinear structures with uncertainties.

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Additional resources for Analysis and Synthesis for Interval Type-2 Fuzzy-Model-Based Systems

Example text

Firstly, the state-feedback and the output-feedback control systems are constructed. A new performance index, referred to extended dissipativity performance, is introduced. The extended dissipativity is a generalization of the H∞ performance, the L2 -L∞ performance, the passivity performance and dissipativity performance. Secondly, based on Lyapunov stability theory, the state and output-feedback controllers are designed respectively to guarantee that the closed-loop system is asymptotically stable with extended dissipativity performance.

Based on the IT2 T–S fuzzy model, the criterion to design the desired controller is obtained, which guarantees the closed-loop system to be asymptotically stable and satisfies the predefined cost function. Moreover, the controller to be designed does not need to share the same premise variables of the system, which enhances the flexibility of controller design and reduces the conservativeness. Chapter 14 draws conclusions on the book, and points out some possible research directions related to the work done in this book.

500. 500 500 The constant scalars are chosen as δ i j1k = wi (x 1,k )m j (x 1,k ), δ i j2k = wi (x 1,k )m j (x 1,k ), δ i j1k = wi (x 1,k )m j (x 1,k ), δ i j2k = wi (x 1,k )m j (x 1,k ). 7 with l = 1 is employed to determine the system stability and synthesize the feedback gains. 8562 . The IT2 fuzzy controller is employed to stabilize the inverted pendulum with m p = 3 kg and Mc = 8 kg. The state responses of the system with different initial sates are shown in Fig. 2, which shows that the inverted pendulum can be stabilized subject to different values of m p and Mc , and different initial conditions.

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Analysis and Synthesis for Interval Type-2 Fuzzy-Model-Based Systems by Hongyi Li, Ligang Wu, Hak-Keung Lam, Yabin Gao

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