基于自适应神经网络的不确定非线性系统的模糊跟踪控制

提出了一种基于模糊模型和自适应神经网络的跟踪控制方法.在系统具有未知不确定非线性特性的情况下,首先利用T＿S模糊模型对系统的已知特性进行近似建模,对基于模糊模型的模糊H∞跟踪控制律进行输出跟踪控制.并在此基础上,进一步采用RBF神经网络完全自适应控制,通过在线自适应调整RBF神经网络的权重、函数中心和宽度,从而有效地消除系统的未知不确定性和模糊建模误差的影响,保证了非线性闭环系统的稳定性和系统的H∞跟踪性能,而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.最后,将所提出的方法应用到一非线性混沌系统,仿真结果表明了所提出的方案不仅能够有效地稳定该混沌系统,而且能使系统输出跟踪期望输出.

Fuzzy tracking control for uncertain nonlinear system based on adaptive neural networ

The tracking control scheme based on fuzzy model and adaptive neural network is presented for a class of nonlinear system with unknown uncertain nonlinearities.Firstly,the Takagi-Sugeno(T-S) fuzzy model was adopted to approximately model the known nonlinearity of the system,and fuzzy-model-based H-infinity tracking control law was designed to track the (desired) output signal.Secondly,full adaptive radial basis function(RBF) neural network control was used to improve the scheme of the fuzzy H-infinity tracking control.The effect of the unknown uncertainties and the error caused by fuzzy modeling was overcome by on-line adaptive tuning of the weights,centers and widths of the RBF neural network,and no matching conditions or constraint conditions were required.It was proved that the proposed control scheme could guarantee the stability of the designed closed loop system and the good H-infinity tracking performance as well.Finally,the proposed scheme was applied to a nonlinear chaos system.The simulation results showed that the proposed method not only can stabilize the chaos systems,but also track the desired output signal.