Abstract: This paper presents the robust stability analysis and design methodology of the fuzzy feedback linearization control systems. Uncertainty and disturbances with known bounds are assumed to be included in the Takagi-Sugeno (TS) fuzzy models representing the nonlinear plants. L2 robust stability of the closed system is analyzed by casting the systems into the diagonal norm bounded linear...

This paper deals with an impulsive stochastic fuzzy cellular neural networks with both time-varying and infinite distributed delays. Based on M-matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and global exponential p−stability of the equilibrium point for the addressed impulsive stochastic fuzzy cellular neural network wit...

In this paper, the discrete-time fuzzy BAM neural network with delays and impulses is studied. Sufficient conditions are obtained for the existence and global stability of a unique equilibrium of this class of fuzzy BAM neural networks with Lipschitzian activation functions without assuming their boundedness, monotonicity or differentiability and subjected to impulsive state displacements at fi...

Alternative LMI Conditions for Takagi-Sugeno Systems Via Fuzzy Lyapunov Function This paper deals with the stability analysis and control design for continuous Takagi-Sugeno fuzzy systems in a linear matrix inequality (LMI) framework. New LMI stability conditions are obtained by applying a relaxation strategy in a recently proposed fuzzy Lyapunov function. In these new LMI stability conditions,...

In this paper, we propose new sets of criteria for exponential robust stability of Takagi-Sugeno (T-S) fuzzy Hopfield neural networks. The L2−L∞ approach is applied to obtain new sets of stability criteria, under which T-S fuzzy Hopfield neural networks reduce the effect of external input to a prescribed level. These sets of criteria are presented based on the matrix norm and linear matrix ineq...

A neuro fuzzy control algorithm for planar robotic manipulators is developed in this paper. This control algorithm is able to handle uncertainties, disturbances or unmodeled dynamics. Asymptotic stability of the equilibrium state of the control system is shown by use of direct Lyapunov method and Barbalat’s Lemma. A series of simulations confirms the algorithm goodness. Key-Words: fuzzy control...

In this paper, the problem of stability analysis for a class of impulsive stochastic fuzzy Cohen-Grossberg neural networks with mixed delays is considered. Based on M-matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and global exponential stability in mean square means of the equilibrium point for the addressed impulsive st...

In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...

The purpose of this paper is to study the description of power system QV curves by using the method of fuzzy modeling for analysis of voltage stability. Voltage instability of power system comes from increasing load rapidly, and causes bus voltage to drop. When voltage is out of control and it can be voltage collapse. The QV curve can identify voltage stability limit, and it determine robustnes...

In this paper difference methods to solve "fuzzy partial differential equations" (FPDE) such as fuzzy hyperbolic and fuzzy parabolic equations are considered. The existence of the solution and stability of the method are examined in detail. Finally examples are presented to show that the Hausdorff  distance between the exact solution and approximate solution tends to zero.