【摘要】 针对非线性系统的内模控制,从理论上分析了神经网络控制器的可实现问题,并且用径向基函数(RBF)神经网络实现内部模型及逆模控制器,同时改进了径向基函数中心的学习算法.仿真结果表明,基于RBF网络的非线性系统内模控制的动态性能较好,对于对象参数扰动具有一定的抗扰性.更多还原

【Abstract】 For the intenal model control of nonlinear system, the possibility to get control is theoretically analyzed on the basis of Artificial Neural Network and practically solved by Radial Basis Function Neural Network(RBFNN). Meanwhile, the proposed method improves the learming algorithm of the center of RBF. The simulation results show that nonlinear intemal model control law based on RBFNN has the following properties: strong robustness and high anti\|interference capability.更多还原