Adaptive neural-network predictive control for nonminimum-phase systems

Wei Wu, Wei Ching Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2006 American Control Conference
Pages2981-2986
Number of pages6
Publication statusPublished - 2006 Dec 1
Event2006 American Control Conference - Minneapolis, MN, United States
Duration: 2006 Jun 142006 Jun 16

Publication series

NameProceedings of the American Control Conference
Volume2006
ISSN (Print)0743-1619

Other

Other2006 American Control Conference
CountryUnited States
CityMinneapolis, MN
Period06-06-1406-06-16

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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