神经网络内模控制在非线性时滞系统中的应用研究

针对工业中常见的时滞现象，提出把内模控制方法和神经控制原理有效结合起来，利用一种改进RBF神经网络对被控对象的模型和控制器进行自适应辨识，通过对实验室电加热炉这种典型一阶滞后对象实验，仿真表明，所提出的方法具有良好的控制特性，在系统受到干扰或对象参数发生变化的情况下，仍然具有良好的自适应性和鲁棒稳定性。

Applied research of neural network internal model control in nonlinear time-delay system

According to the common time-lag in industry, an internal model control method and neural control principle effectively combined in this pape, using an improved RBF neural network to be identified for controlled object model and controller adaptively, Based on laboratory electric heating which is a typical first-order lagging object, simulation experiments show that the proposed method has good control characteristic, when system interference or an object parameter changing because of circumstances, the system still has good adaptability and robust stability.