Adaptive neural-network predictive control for nonminimum-phase systems

Wei Wu, Wei Ching Hsu

研究成果: Conference contribution

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 for neural network architecture associated with prescribed input/output patterns, the feedforward neural network (FNN) is used to capture dynamic and steady-state characteristics of minimum-phase modes over a specified operating range. A one-step-ahead neural prediction algorithm with respect to physical constraints can carry out the offset free performance. Closed-loop simulations demonstrate the effectiveness of the proposed approaches.

原文English
主出版物標題Proceedings of the 2006 American Control Conference
頁面2981-2986
頁數6
出版狀態Published - 2006 十二月 1
事件2006 American Control Conference - Minneapolis, MN, United States
持續時間: 2006 六月 142006 六月 16

出版系列

名字Proceedings of the American Control Conference
2006
ISSN(列印)0743-1619

Other

Other2006 American Control Conference
國家United States
城市Minneapolis, MN
期間06-06-1406-06-16

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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