中国科学院研究生院学报2008，Vol.25Issue(2)：224-232,9.
Hammerstein模型基于神经网络的预测控制方法
Model predictive control based on neural networks for Hammerstein type nonlinear systems
向微 1盛捷 1陈宗海1
作者信息
- 1. 中国科学技术大学自动化系,合肥,230027
- 折叠
摘要
Abstract
The Hammerstein model is composed of a nonlinear static element and a linear dynamic element serially, and it proves to be effective in describing the behavior of many chemical processes. By appropriate identification, the intricate nonlinear control problem of this model can be facilitated into two problems: the control of the linear part and the solution of the nonlinear part. In this paper, a model predictive control scheme is proposed, which uses a set of neural networks to approximate the inverse mapping of the nonlinear block. This neural networks method needn't assume that the nonlinear block is a polynomial equation, thus it overcomes the difficulty that no real roots exist for the polynomial equation. Two simulation examples, including a pH neutralization process, are used to demonstrate the effectiveness of the method.关键词
模型预测控制/Hammerstein模型/神经网络Key words
model predictive control/ Hammerstein model/ neural networks分类
信息技术与安全科学引用本文复制引用
向微,盛捷,陈宗海..Hammerstein模型基于神经网络的预测控制方法[J].中国科学院研究生院学报,2008,25(2):224-232,9.
