针对T-S模型和实际系统之间存在的一般差异，拟提出含有一般建模误差的新模糊T-S模型，用未知变参数化神经网路逼近建模误差，给出由并行分布补偿反馈项和差分-微分型自适应补偿项组成的控制器，利用LMI和鲁棒自适应控制理论，给出系统全局稳定的充分条件；对非线性随机时滞系统，建立带有一般建模误差和马尔可夫切换的多伊藤随机时滞方程的新T-S模糊模型，给出其随机稳定性的条件，设计系统的新鲁棒自适应控制器，利用随机Lyapunov稳定理论给出闭环系统稳定的新双线性矩阵不等式充分条件，并利用新的矩阵分解将一类双线性矩阵不等式转化为线性矩阵不等式，降低求解该问题的难度；给出基于该新模型的系统有限时间随机稳定与收敛的条件及其有限时间随机稳定化的条件；将以上结果推广到随机时滞复杂动态网同步问题，给出分布式自适应同步算法。为实际系统控制问题提供新途径。

In this proposal, according to the differences of the existing T-S fuzzy models and its reality systems,the general modeling errors are approximated by unknown time-varying parametric neural networks,a novel T-S fuzzy model with general modeling errors is proposed.A new control strategy, which consists of a parallel distributed compensation feedback term and difference-differential-type adaptive compensated term, is proposed for the T-S fuzzy model with the general modeling errors. The sufficient condition of the stability for resulting closed-loop system is also given via LMIs and adaptive robust control theory. A novel multiple T-S fuzzy model with modeling errors and Markov switching Ito stochastic time-delay equations is presented for nonlinear stochastic time-delay systems, a new adaptive control strategy is designed. A sufficient condition of stochastic stability in term of bilinear matrix inequatity for the closed-loop systems is given by using stochastic piecewise quadratic Lyapunov-krasovksii function and weight free matrix and furthermore, a class of solving BMIs problem are changed into an LMI terms,so as to reduce greatly the difficulty grade.The condition of the finite-time stochastic stability based on the novel models with the general modeling errors is also proposed and the finite-time stochastic adaptive stablization strategy is established.The obtained results are then extended to the problems of synchronization of the complex stochastic time-delay dynamic networks, proposes a distributed adaptive synchronization algorithm. The proposed approaches will give a new way to solve the control problem of many real systems.