【摘要】 针对基本预测函数控制只能用于线性对象的控制这一不足,提出了基于混合神经网络的非线性预测函数控制.混合神经网络由BP网络和线性神经网络串连组成.采用混合神经网络对可用Hammerstein模型描述的非线性对象进行有效的辨识.其中,BP网络反映了非线性静态增益,线性神经网络反映了线性动态子系统.利用BP网络求出非线性静态增益的逆并与非线性对象串联,抵消非线性对象中的非线性静态增益部分,将非线性对象的控制问题转化为对线性对象的控制问题,实现了对非线性对象的预测函数控制.当被控对象的特性发生变化时,可对混合神经网络权值及时进行修正并调整控制器参数使控制系统始终保持良好的控制性能.仿真结果表明,此控制系统具有良好的控制效果.更多还原

【Abstract】 A basic predictive function control(PFC) is only applicable for linear plant control.To overcome the defect,a nonlinear self-adaptive PFC based on a hybrid neural network is presented.The hybrid neural network is composed of BP network and linear neural network.One can identify a nonlinear plant described with Hammerstein model effectively by the hybrid neural network.The BP network reflects the nonlinear static gain .The linear neural network reflects the linear dynamic subsystem.Then the inverse form of nonlinear static gain is solved and in series with the nonlinear plant to compensate the nonlinear static gain of nonlinear plant.Thus the nonlinear plant control is transformed into linear plant control and the PFC of nonlinear plant is realized.The control system can adjust the weights of hybrid neural network and the parameters of controller timely to keep good control performance when the character of controlled plant varies.Simulation results show that the control system has good control effect.更多还原