Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 9, NO. 5, SEPTEMBER 2001 741 Neuro-Predictive Process Control Using On-Line
 

Summary: IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 9, NO. 5, SEPTEMBER 2001 741
Neuro-Predictive Process Control Using On-Line
Controller Adaptation
Alexander G. Parlos, Senior Member, IEEE, Sanjay Parthasarathy, and Amir F. Atiya, Senior Member, IEEE
Abstract--A novel architecture for integrating neural networks
with industrial controllers is proposed, for use in predictive
control of complex process systems. In the proposed method, a
conventional controller, e.g., a proportional-integral (PI) con-
troller, is used to control the process. In addition, a recurrent
neural network is used in the form of a multistep-ahead predictor
(MSP) to model the process dynamics. The parameters of the
PI controller are tuned by a backpropagation-through-time
(BTT)-like approach using "parallel learning" to achieve accept-
able regulation and stabilization of the controlled process. The
advantage of such a formulation is the effective on-line adaptation
of the controller parameters while the process is in operation,
and the tracking of the different process operating regimes and
variations. The proposed method is used in the stabilization and
transient control of u-tube steam generator (UTSG) water level.
Currently, available constant-gain PI controllers are unable to

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology
Parlos, Alexander - Department of Mechanical Engineering, Texas A&M University

 

Collections: Computer Technologies and Information Sciences; Engineering