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Title: Adaptive model predictive process control using neural networks

Abstract

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

Inventors:
; ;
Issue Date:
Research Org.:
University of California
Sponsoring Org.:
USDOE, Washington, DC (United States)
OSTI Identifier:
527745
Patent Number(s):
5,659,667
Application Number:
PAN: 8-373,736
Assignee:
Univ. of California Office of Technology Transfer, Alemeda, CA (United States) PTO; SCA: 320303; PA: EDB-97:125344; SN: 97001843784
DOE Contract Number:  
W-7405-ENG-36
Resource Type:
Patent
Resource Relation:
Other Information: PBD: 19 Aug 1997
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; PROCESS CONTROL; NEURAL NETWORKS; CONTROL SYSTEMS; INDUSTRIAL PLANTS; TRAINING; ON-LINE SYSTEMS

Citation Formats

Buescher, K.L., Baum, C.C., and Jones, R.D. Adaptive model predictive process control using neural networks. United States: N. p., 1997. Web.
Buescher, K.L., Baum, C.C., & Jones, R.D. Adaptive model predictive process control using neural networks. United States.
Buescher, K.L., Baum, C.C., and Jones, R.D. Tue . "Adaptive model predictive process control using neural networks". United States.
@article{osti_527745,
title = {Adaptive model predictive process control using neural networks},
author = {Buescher, K.L. and Baum, C.C. and Jones, R.D.},
abstractNote = {A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1997},
month = {8}
}