基于模糊模型和神经网络的多时滞不确定非线性系统的鲁棒H∞控制

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.

Robust H∞ Control for Multiple Time-delay Uncertain Nonlinear System Based on Fuzzy Model and Neural Network

A mixed control method combining fuzzy model-based control and neural network control is presented for a class of uncertain nonlinear system with multiple time delays. Firstly, fuzzymodel-based H∞ control law is designed by means of LMI method for time-delay nonlinear multiinput systems modeled by the fuzzy T-S model with multiple time delays. Secondly, full adaptive RBF neural network control method is used to improve the scheme of the fuzzy H∞ control. The effect of the unknown uncertainties and the error caused by fuzzy modeling is overcome by adaptive tuning of the weights, centers and widths of the RBF neural network on line, and no matching conditions or constraint conditions are required. The stability of the designed closed loop system is proved. The effectiveness of the proposed method is finally demonstrated through simulation on the multiple time-delay nonlinear chaos system.