Method and apparatus for attenuating error in dynamic and steady-state processes for prediction, control, and optimization
First Claim
1. A method for predicting an output value from a received input value, the method comprising:
- on a physical computing device;
modeling a set of static data received from a plant or process in a predictive static model over a first range, the static model having a static gain K and modeling static operation of the plant or process;
modeling a set of dynamic data received from the plant or process in a predictive dynamic model over a second range smaller than the first range, the dynamic model having a dynamic gain and modeling dynamic operation of the plant or process over the second range, and the dynamic model being independent of operation of the static model;
adjusting the dynamic gain of the dynamic model as a predetermined function of the static gain K of the static model to vary model parameters of the dynamic model;
predicting the dynamic operation of the plant or process from an initial steady-state input value at a first time to a predicted steady-state input value at a second time to determine a predicted dynamic operation;
comparing the predicted dynamic operation to a desired steady-state value of the plant or process at a final desired output value and generating an error as the difference therebetween;
attenuating the error as a function of time between the first time and the second time;
determining a change in the input value for input to said predicting the dynamic operation which is operable to vary the input value thereto; and
varying the change in the input value in accordance with the determined change to minimize the attenuated error.
4 Assignments
0 Petitions
Accused Products
Abstract
A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bi variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). 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.
112 Citations
20 Claims
-
1. A method for predicting an output value from a received input value, the method comprising:
-
on a physical computing device; modeling a set of static data received from a plant or process in a predictive static model over a first range, the static model having a static gain K and modeling static operation of the plant or process; modeling a set of dynamic data received from the plant or process in a predictive dynamic model over a second range smaller than the first range, the dynamic model having a dynamic gain and modeling dynamic operation of the plant or process over the second range, and the dynamic model being independent of operation of the static model; adjusting the dynamic gain of the dynamic model as a predetermined function of the static gain K of the static model to vary model parameters of the dynamic model; predicting the dynamic operation of the plant or process from an initial steady-state input value at a first time to a predicted steady-state input value at a second time to determine a predicted dynamic operation; comparing the predicted dynamic operation to a desired steady-state value of the plant or process at a final desired output value and generating an error as the difference therebetween; attenuating the error as a function of time between the first time and the second time; determining a change in the input value for input to said predicting the dynamic operation which is operable to vary the input value thereto; and varying the change in the input value in accordance with the determined change to minimize the attenuated error. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
-
-
11. A computer-accessible memory medium for predicting an output value from a received input value, the memory medium comprising computer instructions executable by a processor to perform:
-
modeling a set of static data received from a plant or process in a predictive static model over a first range, the static model having a static gain K and modeling static operation of the plant or process; modeling a set of dynamic data received from the plant or process in a predictive dynamic model over a second range smaller than the first range, the dynamic model having a dynamic gain k and modeling dynamic operation of the plant or process over the second range, and the dynamic model being independent of operation of the static model; adjusting the dynamic gain k of the dynamic model as a predetermined function of the static gain K of the static model to vary model parameters of the dynamic model; predicting the dynamic operation of the plant or process from an initial steady-state input value at a first time to a predicted steady-state input value at a second time to determine a predicted dynamic operation; comparing the predicted dynamic operation to a desired steady-state value of the plant or process at a final desired output value and generating an error as the difference therebetween; attenuating the error as a function of time between the first time and the second time; determining a change in the input value for input to said predicting the dynamic operation which is operable to vary the input value thereto; and varying the change in the input value in accordance with the determined change to minimize the attenuated error. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
-
-
19. A system for predicting an output value from a received input value, comprising:
-
a processor; a predictive static model implemented by the processor, wherein the predictive static models has a static gain K and models static operation of the plant or process, and wherein the predictive static model comprises a set of static data over a first range received from a plant or process; a dynamic model coupled to the predictive static model and implemented by the processor, wherein the dynamic model has a dynamic gain k and models dynamic operation of the plant or process, and wherein the dynamic model comprises a set of dynamic data over a second range smaller than the first range received from the plant or process; wherein the dynamic model is operable to; adjust the dynamic gain k as a predetermined function of the static gain K of the static model to vary model parameters of the dynamic model; predict the dynamic operation of the plant or process from an initial steady-state input value at a first time to a predicted steady-state input value at a second time to determine a predicted dynamic operation; compare the predicted dynamic operation to a desired steady-state value of the plant or process at a final desired output value and generate an error as the difference therebetween; attenuate the error as a function of time between the first time and the second time; determine a change in the input value for input to the dynamic model which is operable to vary the input value thereto; and vary the change in the input value in accordance with the determined change to minimize the attenuated error.
-
-
20. A system, comprising:
-
means for modeling a set of static data received from a plant or process in a predictive static model over a first range, the static model having a static gain K and modeling static operation of the plant or process; means for modeling a set of dynamic data received from the plant or process in a predictive dynamic model over a second range smaller than the first range, the dynamic model having a dynamic gain k and modeling dynamic operation of the plant or process over the second range, and the dynamic model being independent of operation of the static model; means for adjusting the dynamic gain k of the dynamic model as a predetermined function of the static gain K of the static model to vary model parameters of the dynamic model; means for predicting the dynamic operation of the plant or process from an initial steady-state input value at a first time to a predicted steady-state input value at a second time to determine a predicted dynamic operation; means for comparing the predicted dynamic operation to a desired steady-state value of the plant or process at a final desired output value and generating an error as the difference therebetween; means for attenuating the error as a function of time between the first time and the second time; means for determining a change in the input value for input to said predicting the dynamic operation which is operable to vary the input value thereto; and means for varying the change in the input value in accordance with the determined change to minimize the attenuated error.
-
Specification