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This paper proposes a line integral Lyapunov function approach to stabilityanalysis and stabilization for It\^o stochastic T-S models. Unlike thedeterministic case, stability analysis of this model needs the information ofHessian matrix of the line integral Lyapunov function which is related topartial derivatives of the basis functions. By introducing a new method tohandle these partial derivatives and using the property of state-dependentmatrix with rank one, the stability conditions of the underlying system can beestablished via a line integral Lyapunov function. These conditions obtainedare more general than the ones which are based on quadratic Lyapunov functions.Based on the stability conditions, a controller is developed by conecomplementarity linerization algorithm. A non-quadratic Lyapunov functionapproach is thus proposed for the stabilization problem of the It\^o stochasticT-S models. It has been shown that the problem can be solved by optimizing sumof traces for a group of products of matrix variables with linear constraints.Numerical examples are given to illustrate the effectiveness of the proposedcontrol scheme.

Stabilization of It\^o Stochastic T-S Models via Line Integral and Novel Estimate for Hessian Matrices