基于混合径向基神经网络的建模及其逆模控制研究  

Modeling Based on Hybrid Radial Basis Function Neural Networks and Its Backward Model Control

作　　者：陈宗海[1] 苑明哲[2] 向微[1] 张彦武[2] 

机构地区：[1]中国科学技术大学自动化系,合肥230027 [2]中国科学院沈阳自动化研究所工业控制研究室,沈阳110016

出　　处：《模式识别与人工智能》2006年第6期734-738,共5页Pattern Recognition and Artificial Intelligence

摘　　要：传统的基于机理或局部线性化模型的控制策略不足以解决越来越复杂的控制问题,而神经网络用于控制也存在泛化能力差等缺陷,因此本文提出一种将被控对象已知机理和RBF神经网络结合起来卖现逆模控制的方法,一方面能发挥神经网络非线性逼近的强大功能,另一方面利用被控对象已知机理信息指导神经网络的收敛方向,改进神经网络的泛化能力。由此方法设计的逆模控制器,在保证控制精度的前提下,速度远快于标准径向基神经网络逆模控制器,且对扰动、时延、非线性及对象参数的摄动有较强的适应能力,具有良好的控制品质。Traditional control methods are not satisfactory in more and more complex process control, and the generalization ability of neural networks in control is weak. In this paper, a novel structure, the combinations of the process fundamentals and RBFNN is presented to direct the neural network convergence and exert the excellent capability on nonlinear approach of neural networks. Simulation results show that the compute velocity of the backward model controller using the hybrid RBFNN, while the control precision index is ensured, is much higher than the backward model controllers using common RBFNN. The hybrid RBFNN backward model controller also has excellent control quality and shows good adaptation to disturbance, time delay, nonlinear and the drift of plant parameters.

关 键 词：标准径向基神经网络 混合径向基神经网络 机理模型 逆模控制 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]