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

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.