Referenced Items are not available for this document.
A neural-network-based technique for developing nonlinear dynamic models from empirical data for an model predictive control (MPC) algorithm is presented. These models can be derived for a wide variety of processes and can also be used efficiently in an MPC framework. The nonlinear MPC-based approach presented has been successfully implemented in a number of industrial applications in the refining, petrochemical, pulp and paper, power, and food industries. Performance of the controller on a nonlinear industrial process, a polyethylene reactor, and a simulated continuous stirred tank reactor is presented
Published in:
Control Systems, IEEE
(Volume:20
,
Issue:
3
)
Date of Publication: Jun 2000
- Page(s):
- 53 - 62
- ISSN :
- 1066-033X
- INSPEC Accession Number:
- 6609812
- Digital Object Identifier :
- 10.1109/37.845038
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Jun 2000
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