Adaptive neural-network predictive control for nonminimum-phase nonlinear processes

被引:0
|
作者
Wu, Wei [1 ]
Hsu, Wei-Ching [1 ]
机构
[1] Natl Yunlin Univ Sci & Technol, Dept Chem Engn, Douliou 64002, Yunlin, Taiwan
关键词
adaptive mechanism; feed-forward neural network; nonminimum-phase systems; predictive control;
D O I
10.1080/00986440600716043
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
An adaptive neural-network predictive control strategy for a class of nonlinear processes, which exhibit input multiplicities and change in the sign of steady-state gains, is presented. According to the graphic-based determination associated with prescribed input/output patterns, the feed-forward neural network (FNN) is attributed to reconstruct dynamic and steady-state characteristics of minimum-phase modes with specified operating ranges. The flexible predictive control strategy using on-line neuro-based adaptation is developed for enhancing the predictive capability of neural network. Finally, the proposed FNN-based implementation is illustrated on simulations of both isothermal and adiabatic CSTR systems.
引用
收藏
页码:177 / 193
页数:17
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