Paper Info
Title
Adaptive neural-network predictive control for nonminimum-phase systems
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
Year
DOI
Venue
2006
10.1109/ACC.2006.1657173
Minneapolis, MN
Keywords
DocType
Volume
mimo systems,adaptive control,closed loop systems,feedforward neural nets,neurocontrollers,nonlinear control systems,predictive control,closed-loop simulations,feedforward neural network,input multiplicities,input/output patterns,neural network architecture,neural-network control,nonlinear processes,nonminimum-phase systems,one-step-ahead neural prediction,feedforward neural networks,neural networks,nonlinear systems,neural network,input output,steady state,predictive models,fuzzy control
Conference
1-12
ISSN
ISBN
Citations 
0743-1619
1-4244-0210-7
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Wu Wei120414.84
Wei-Ching Hsu200.34