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Adaptive model predictive process control using neural networks

United States Patent

5,659,667
August 19, 1997
View the Complete Patent at the US Patent & Trademark Office
Los Alamos National Laboratory - Visit the Technology Transfer Division Website
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.
Buescher; Kevin L. (Los Alamos, NM), Baum; Christopher C. (Mazomanie, WI), Jones; Roger D. (Espanola, NM)
The Regents of the University of California Office of Technology Transfer (Alemeda, CA)
08/ 373,736
January 17, 1995
BACKGROUND OF THE INVENTION This invention relates to process control systems and, more particularly, to model predictive control systems using neural nets. This invention was made with government support under Contract No. W-7405-ENG-36 awarded by the U.S. Department of Energy. The government has certain rights in the invention.