基于神经网络的迭代学习控制方法在间歇聚合反应中的应用.pdf

1 16024 E-mail: xhgao2005@ ABS The research of ILC based on Neural Network and its Application to polymerization reactor Gao Xing hangShao Cheng (Department of electrical and information engineering from Dalian university of technology Liao Ning Da Lian 1 16024) E-mail xhgao2005@ Abstract: The dynamic characteristic of polymeric reaction is time-variantnon-linear and so on. The traditional control algorithms can not satisfy these requests and requisite tracking precision. A new iterative learning control (ILC) algorithm based on neural network optimization is proposed in this paper. It introduced how to design the iterative learning controller according to ILC theory, and proposed that BP neural network optimizes and calculates the parameters of the Iterative learning controller. Meanwhile, the optimal iterative control algorithm is used to the temperature control of ABS resin polymerization reactor. The simulation result indicates that the algorithm is much effective and can approach anticipant contrail with less iterativedouble-quick convergence and lofty tracking precision. Keywords: Iterative Learning Control; Neural Network; Parameters Optimization; Polymerization Reaction [1]