基于径向基函数神经网络的Lorenz混沌系统滑模控制

针对受参数不确定和外扰影响的混沌Lorenz系统，提出一种基于径向基函数（RBF）神经网 络的滑模控制方法.基于被控系统在不稳定平衡点处状态误差的可控规范形，设计滑模切换 面并将其作为神经网络的唯一输入.单入单出形式的RBF控制器隐层只需7个径向基函数，网 络的权值则依滑模趋近条件在线确定.仿真表明该控制器对系统参数突变和外部干扰具有鲁棒性，同时抑制了抖振. 关键词： 混沌控制 滑模 径向基函数神经网络 Lorenz系统

Chaos control of Lorenz system via RBF neuralnetwork sliding mode controller

A novel adaptive radial basis function(RBF) neural network sliding mode strat egy is developed to con trol Lorenz chaos with parametric uncertainties and external disturbances. Based on the controllable canonical form of system state error at its unstable equili brium, a sliding surface is defined as the only input to the RBF controller. Onl y seven RBFs are required for the controller and their weights ar e trained on-line based on the sliding surface approaching condition. The simula tio n results show that this method is feasible and effective, and the robustness to parametric uncertainties and external disturbance is provided.