E.S. Tognetti

Publications (2)0 Total impact

• Source

  Available from: ntnu.no

  Conference Proceeding: Improved stabilization conditions for Takagi-Sugeno fuzzy systems via fuzzy integral lyapunov functions

  E.S. Tognetti, R.C.L.F. Oliveira, P.L.D. Peres
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  ABSTRACT: This paper presents new results concerning the design of state feedback controllers for continuous-time Takagi Sugeno (T-S) fuzzy systems. The conditions, based on a line integral fuzzy Lyapunov function, are specially suitable for T-S fuzzy systems where no information about the time-derivatives of the membership functions is available. The controller is designed through linear matrix inequalities in a two step procedure: at the first step, a stabilizing fuzzy controller is obtained for a relaxed frozen (i.e. time-invariant) T-S fuzzy system. This control gain is then used as an input data at the second step, that provides a stabilizing control law guaranteed by the line-integral Lyapunov function. An extension to cope with H_∞ guaranteed cost control of T-S fuzzy systems is also provided. Numerical examples illustrate the advantages of the proposed method when compared to other techniques available in the literature.
  American Control Conference (ACC), 2011; 08/2011
• Conference Proceeding: Selective stabilization of Takagi-Sugeno fuzzy systems

  E.S. Tognetti, R.C.L.F. Oliveira, P.L.D. Peres
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  ABSTRACT: This paper presents new results concerning the stability analysis and the design of state feedback controllers for continuous-time Takagi-Sugeno (T-S) fuzzy systems via fuzzy Lyapunov functions. Using the Cartesian product of simplexes, called multi-simplex, a new modeling is proposed to represent the membership functions of T-S fuzzy systems. In the multi-simplex representation, linear matrix inequality relaxations based on homogeneous polynomials matrices are provided for stability analysis and controller design. The time-derivatives of the membership functions are modeled as belonging to polytopic convex sets and may be considered unbounded if nothing is known about their upper limits. As main aspect, the method allows to synthesize control gains depending only on some premise variables selected by the designer. Numerical experiments illustrate the flexibility and advantages of the method.
  Fuzzy Systems (FUZZ), 2010 IEEE International Conference on; 08/2010