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针对一类具有多时滞的不确定非线性系统 ,提出了一种基于模糊模型和神经网络的组合控制方法 .利用具有多时滞的模糊T S模型对系统进行近似建模并给出基于线性矩阵不等式(LMI)的模糊H∞ 控制律 .提出完全自适应RBF神经网络控制方法 ,通过在线自适应调整RBF神经网络的权重、函数中心和宽度 ,来对消系统的未知不确定性和模糊建模误差的影响 ,不要求系统的不确定项和模糊建模误差满足任何匹配条件或约束 ,并证明了闭环系统的稳定性 .最后 ,将所提出的方法应用到一具有多时滞的非线性混沌系统 ,仿真结果表明了该方法的有效性 .
Aiming at a class of uncertain nonlinear systems with time-delay, a hybrid control method based on fuzzy model and neural network is proposed. The fuzzy TS model with multiple delays is used to approximate the system and the linear matrix inequalities LMI) fuzzy H∞ control law is proposed.Furthermore, a fully adaptive RBF neural network control method is proposed to estimate the unknown uncertainties and fuzzy modeling errors of the system by adaptively adjusting the weight, function center and width of the RBF neural network online The system uncertainty and fuzzy modeling error are not required to satisfy any matching conditions or constraints and the stability of the closed-loop system is proved.Finally, the proposed method is applied to a nonlinear chaotic system with multiple delays and the simulation The results show the effectiveness of this method.