Method and apparatus for modeling dynamic and steady-state processes for prediction, control and optimization

• US 6,487,459 B1
• Filed: 02/16/1999
• Issued: 11/26/2002
• Est. Priority Date: 05/06/1996
• Status: Expired due to Term

• Current Assignee: Rockwell Automation Technologies Incorporated (Rockwell Automation, Inc.)
• Original Assignee: Pavilion Technologies Incorporated (Rockwell Automation, Inc.)
• Inventors: Piche, Stephen, Martin, Gregory D., Boe, Eugene, Havener, John P., Gerules, Mark, Timmer, Douglas, Keeler, James David
• Primary Examiner(s): Cuchlinski, Jr., William A.
• Assistant Examiner(s): Marc, McDieunel, Marc, McDieunel
• Application Number:
  US09/250,432
• Time in Patent Office: 1,379 Days
• Field of Search: 700/44, 700/46, 700/71, 700/29, 700/273, 700/288, 700/269, 700/37, 700/39, 375/316, 375/329, 375/350, 327/185, 327/198, 701/23, 703/2, 703/18
• US Class Current: 700/44
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G05B 17/02 electric

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 b_i variables are scaled by the ratio of the gains K and k. The b_i 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.

Claims

1. A dynamic controller for controlling the operation of a system by predicting a change in the dynamic input values to the system to effect a change in the output from a current output value at a first time to a desired output value at a second time, comprising:

a dynamic predictive model for receiving the current input value and the desired output value and predicting a plurality of input values at different time positions between the first time and the second time to define a dynamic operation path of the system between the current output value and the desired output value at the second time; and

an optimizer for optimizing the operation of the dynamic controller at each of the different time positions from the first time to the second time in accordance with a predetermined optimization method that optimizes the objectives of the dynamic controller to achieve a desired path, such that the objectives of the dynamic predictive model varies as a function of time.

2. The dynamic controller of claim 1, wherein said dynamic predictive model comprises:

a dynamic forward model operable to receive input values at each of said time positions and map said received input values through a stored representation of the system to provide a predicted dynamic output value;

an error generator for comparing the predicted dynamic output value to the desired output value and generating a primary error value as the difference therebetween for each of said time positions;

an error minimization device for determining a change in the input value to minimize the primary error value output by said error generator;

a summation device for summing said determined input change value with the original input value for each time position to provide a future input value; and

a controller for controlling the operation of said error minimization device to operate under control of said optimizer to minimize said primary error value in accordance with said predetermined optimization method.

3. The dynamic controller of claim 2, wherein said controller controls the operation of said summation device to iteratively minimize said primary error value by storing the summed output from said summation device in a latch in a first pass through said error minimization device and input the latch contents to said dynamic forward model in subsequent pass and for a plurality of subsequent passes, with the output of said error minimization device summed with the previous contents of said latch with said summation device, said latch containing the current value of the input on the first pass through said dynamic forward model and said error minimization device, said controller outputting the contents of said latch as the input to the system after said primary error value has been determined to meet the objectives in accordance with said predetermined optimization method.

4. The dynamic controller of claim 2, wherein said dynamic forward model is a dynamic linear model with a fixed gain.

5. The dynamic controller of claim 4 and further comprising a gain adjustment device for adjusting the gain of said linear model for substantially all of said time positions.

6. The dynamic controller of claim 5, wherein said gain adjustment device comprises:

a non-linear model for receiving an input value and mapping the received input value through a stored representation of the system to provide on the output thereof a predicted output value, and having a non-linear gain associated therewith;

said linear model having parameters associated therewith that define the dynamic gain thereof; and

a parameter adjustment device for adjusting the parameters of said linear model as a function of the gain of said non-linear model for at least one of said time positions.

7. The dynamic controller of claim 6, wherein said gain adjustment device further comprises an approximation device for approximating the dynamic gain for a plurality of said time positions between the value of the dynamic gain at said first time and the determined dynamic gain at the one of said time positions having the dynamic gain thereof determined by said parameter adjustment device.

8. The dynamic controller of claim 7, wherein the one of said time positions at which said parameter adjustment device adjusts said parameters as a function of the gain of said non-linear model corresponds to the maximum at the second time.

9. The dynamic controller of claim 6, wherein said non-linear model is a steady-state model.

10. The dynamic controller of claim 2, wherein said error minimization device includes a primary error modification device for modifying said primary error value to provide a modified error value, said error minimization device optimizing the operation of the dynamic controller to minimize said modified error value in accordance with said predetermined optimization method.

11. The dynamic controller of claim 10, wherein said primary error value is weighted as a function of time from the first time to the second time.

12. The dynamic controller of claim 11, wherein said weighting function decreases as a function of time such that said primary error value is attenuated at a relatively high value proximate to the first time and attenuated at a relatively low level proximate to the second time.

13. The dynamic controller of claim 2, wherein said error minimization device receives said predicted output value from said dynamic forward model and determines a change in the input value maintaining a constraint on the predicted output value such that minimization of the primary error value through a determined input change would not cause said predicted output value from said dynamic forward model to exceed said constraint.

14. The dynamic controller of claim 2, and further comprising a filter determining the operation of said error minimization device when the difference between the predicted dynamic output and the desired output value is insignificant.

15. The dynamic controller of claim 14, wherein said filter determines when the difference between the predicted dynamic output and the desired output value is not significant by determining the accuracy of the model upon which the dynamic forward model is based.

16. The dynamic controller of claim 15, wherein the accuracy is determined as a function of the standard deviation of the error and a predetermined confidence level, wherein said confidence level is based upon the accuracy of the training over the mapped space.