The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 03, 1999

Filed:

May. 06, 1996
Applicant:
Inventors:

Gregory D Martin, Georgetown, TX (US);

Eugene Boe, Austin, TX (US);

Stephen Piche, Austin, TX (US);

James David Keeler, Austin, TX (US);

Douglas Timmer, Austin, TX (US);

Mark Gerules, Cedar Park, TX (US);

John P Havener, Liberty Hill, TX (US);

Assignee:

Pavilion Technologies, Inc., Austin, TX (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G05B / ;
U.S. Cl.
CPC ...
364164 ; 364148 ; 364149 ; 364157 ; 364159 ; 364495 ; 364501 ; 36474503 ; 364747 ; 36473601 ; 395 22 ;
Abstract

A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model and an independent dynamic model. The static model is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model is trained over a narrow range of data. The gain K of the static model is utilized to scale the gain k of the dynamic model. The forced dynamic portion of the model referred to as the b.sub.i variables are scaled by the ratio of the gains K and k. The b.sub.i have a direct effect on the gain of a dynamic model. This is facilitated by a coefficient modification block. Thereafter, the difference between the new value input to the static model and the prior steady-state value is utilized as an input to the dynamic model. The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y. Additionally, the path that is traversed between steady-state value changes.


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