分数阶Chen混沌系统的径向基函数神经滑模控制

针对带有参数扰动和外部干扰的分数阶Chen混沌系统, 提出一种径向基函数(RBF)神经滑模控制方法. 设计滑模切换函数, 将其作为RBF神经网络的唯一输入, 网络的权值可依据滑模趋近条件在线调整. 基于Lyapunov稳定性理论, 分析了该方法的稳定性. 仿真结果表明该控制方法简化了常规神经网络控制结构的复杂性, 削弱了滑模控制的抖振程度, 对参数扰动和外部干扰具有较好的鲁棒性.

Chaos control of fractional order Chen system via radial basis function neural network

A radial basis function(RBF) neural network sliding mode controller for fractional order Chen system with parametric perturbation and external disturbances is presented. The sliding surface is designed as the only input to the RBF neural network and the weights of the network can be adjusted on-line according to the reaching law. Based on the Lyapunov stability theorem, we performed the stability analysis for the controller. The simulation results show that the proposed method simplifies the complex structu...