Adaptive neural-network predictive control for nonminimum-phase systems

Wei Wu, Wei Ching Hsu

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


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
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
ISSN (Print)0743-1619


Other2006 American Control Conference
Country/TerritoryUnited States
CityMinneapolis, MN

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


Dive into the research topics of 'Adaptive neural-network predictive control for nonminimum-phase systems'. Together they form a unique fingerprint.

Cite this