摘要
针对一类具有饱和非线性输入的混沌系统,基于RBF神经网络的逼近能力提出一种控制方案。该方法利用自适应控制和鲁棒控制,使系统可在模型函数和外扰未知下,设计出结构简单有效的控制器,有效消除了现实中由于饱和非线性输入的存在而引起的控制器抖动的不良控制效果。仿真结果表明了所提控制方法的可行性。
Based on the approximation capability of radial basis function neural networks （ RBF NN） , an a- daptive control scheme is proposed for a class of uncertain chaotic systems with saturation nonlinearity input in this paper. Using the adaptive control and robust control, a controller of simple structure and availability is developed under the condition that both model functions and disturbances are unknown. The controller eliminates the chattering in the controlling for the existence of saturation nonlinearity input. Simulation results demonstrate the effectiveness of the proposed approach.
出处
《扬州职业大学学报》
2013年第1期35-38,共4页
Journal of Yangzhou Polytechnic College
关键词
混沌系统
饱和
非线性输入
神经网络控制
自适应控制
chaotic systems
saturation
nonlinear input
neural network control
adaptive control
