A new fuzzy Lyapunov function approach for a Takagi-Sugeno fuzzy control system design

By Bong-Jae Rhee, Sangchul Won. Fuzzy Sets Syst., 2006

490 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/ff414ce1176f6f7101d9f74b84b62920de6f18c0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Huijun Gao
2. Steven X. Ding
3. Shen Yin
4. Luka Eciolaza
5. Tadanari Taniguchi
6. Michio Sugeno
7. Didier Maquin
8. Jianbin Qiu

In Control and Systems Engineering

1. Shen Yin
2. Jianbin Qiu
3. Benoît Marx
4. Dalil Ichalal
5. José Ragot
6. Steven X. Ding
7. Didier Maquin
8. Huijun Gao

Citations in the corpus

• A survey on industrial applications of fuzzy control (2011, unknown): “Popular approaches employ quadratic, piecewise quadratic, non-quadratic, parameter-dependent, polynomial and fuzzy Lyapunov functions [169–182], and they show the constant effort to reduce the conservativeness of the stability conditions.”
• Fuzzy Control Systems: Past, Present and Future (2019, background): “…(PLFs) [30], [32], [33], fuzzy Lyapunov functions (FLFs) depending on the MFs [31], [34], [35], line integral Lyapunov functions (LILFs) [34], [36], [37], polynomial Lyapunov functions depending on the MFs with arbitrary degree [38]–[40], multidimensional fuzzy Lyapunov functions [41], [42],…”
• Fuzzy-Model-Based Reliable Static Output Feedback $\mathscr{H}_{\infty }$ Control of Nonlinear Hyperbolic PDE Systems (2016, background): “Based on [32], [56], and [57], it can be easily verified that V (t, ρ(t)) given in (23) is a Lyapunov function candidate.”
• A systematic approach to improve multiple Lyapunov function stability and stabilization conditions for fuzzy systems (2009, methodology): “For more details on this specific notation see [26].”
• Design of Observers for Takagi-Sugeno Systems with Immeasurable Premise Variables: an ℒ2 Approach (2008, background): “Proof. Using the concept of D-stability presented in (Chilali, 1996) and ( Bong-Jae and Sangchul, 2006 ), the constraint to place the poles of the matrix (A0 − GiC) in the shaded region of Fig. 1 can be expressed in terms of LMIs as :”