Method and apparatus for minimizing error in dynamic and steady-state processes for prediction, control, and optimization

• US 9,329,582 B2
• Filed: 09/10/2012
• Issued: 05/03/2016
• Est. Priority Date: 05/06/1996
• Status: Expired due to Term

• Current Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Original Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Inventors: Boe, Eugene, Piche, Stephen, Martin, Gregory D.
• Primary Examiner(s): Rodriguez, Carlos Ortiz
• Application Number:
  US13/608,578
• Publication Number:
  US 20130055021A1
• Time in Patent Office: 1,331 Days
• Field of Search: 700/79
• US Class Current: 1/1
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G09B 23/02 for mathematics for statics or dynamics G09B23/08

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. 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.

Claims

1. A dynamic controller, comprising:

a dynamic predictive model for receiving a current input value from a plant or process during operation of the plant or process, and a desired output value, and predicting a plurality of input values at a plurality of intermediate times between a first time and a second time to define a dynamic operation path of the plant or process during operation of the plant or process between a current output value at the first time and the desired output value at the second time;

an error generator for comparing a predicted dynamic output value from the dynamic operation path to the desired output value, and generating a primary error value as a difference between the predicted dynamic output value and the desired output value at each of the plurality of intermediate times;

an error minimization device for determining a change in each input value to minimize the primary error value generated by the error generator, wherein the error minimization device determines the change in each input value when the error minimization device is in an operable mode, which is determined based on the primary error value and an error constraint during operation of the plant or process;

a summation device for summing the determined change in each input value with an original input value to provide a future input value as a summed input value, wherein the original input value comprises an input value before each intermediate time; and

a controller for controlling operation of the error minimization device to minimize the primary error value during operation of the plant or process in accordance with an objective of the controller to control operation of the plant or process.

2. The dynamic controller of claim 1,

wherein the error minimization device is placed into the operable mode when the primary error value is below an error tolerance and the error minimization device is taken out of the operable mode when the primary error value is above the error tolerance.

3. The dynamic controller of claim 2,

wherein the error tolerance is based on a function of dynamic gain at the first time and dynamic gain at the second time.

4. The dynamic controller of claim 2,

wherein the error tolerance is based on a ratio of dynamic gain at the first time and dynamic gain at the second time.

5. The dynamic controller of claim 2,

wherein the error tolerance is based on a magnitude of dynamic gain at the first time and/or dynamic gain at the second time.

6. The dynamic controller of claim 2,

wherein the error tolerance is based on a simple average of dynamic gain at the first time and dynamic gain at the second time.

7. The dynamic controller of claim 2,

wherein the error tolerance is based on a weighted average of dynamic gain at the first time and dynamic gain at the second time.

8. The dynamic controller of claim 2,

wherein the error tolerance is based on a linear interpolation between dynamic gain at the first time and dynamic gain at the second time, inclusively.

9. A dynamic controller, comprising:

a dynamic predictive model for receiving a current input value from a plant or process during operation of the plant or process, and a desired output value, and predicting a plurality of input values at a plurality of intermediate times between a first time and a second time to define a dynamic operation path of the plant or process during operation of the plant or process between a current output value at the first time and the desired output value at the second time;

an error generator for comparing a predicted dynamic output value from the dynamic operation path to the desired output value, and generating a primary error value as a difference between the predicted dynamic output value and the desired output value at each of the plurality of intermediate times;

an error minimization device for determining a change in each input value to minimize the primary error value generated by the error generator, wherein the error minimization device is operable to minimize an objective function of the dynamic predictive model with respect to a constraint, and wherein the error minimization device comprises an error constraint to determine operation of the error minimization device with respect to the constraint of the objective function during operation of the plant or process;

a summation device for summing the determined change in each input value with an original input value to provide a future input value as a summed input value, wherein the original input value comprises an input value before each intermediate time; and

a controller for controlling operation of the error minimization device to minimize the primary error value during operation of the plant or process in accordance with an objective of the controller to control operation of the plant or process.

10. The dynamic controller of claim 9,

wherein the error minimization device is operable to minimize the objective function with respect to the constraint when the primary error value is above the error constraint, and wherein the error minimization device is not operable to minimize the objective function with respect to the constraint when the primary error value is below the error constraint.

11. The dynamic controller of claim 10,

wherein the error constraint is based on a function of dynamic gain at the first time and dynamic gain at the second time.

12. The dynamic controller of claim 10,

wherein the error constraint is based on a ratio of dynamic gain at the first time and dynamic gain at the second time.

13. The dynamic controller of claim 10,

wherein the error constraint is based on a magnitude of dynamic gain at the first time and dynamic gain at the second time.

14. The dynamic controller of claim 10,

wherein the error constraint is based on a simple average of dynamic gain at the first time and dynamic gain at the second time.

15. The dynamic controller of claim 10,

wherein the error constraint is based on a weighted average of dynamic gain at the first time and dynamic gain at the second time.

16. The dynamic controller of claim 10,

wherein the error constraint is based on a linear interpolation between dynamic gain at the first time and dynamic gain at the second time, inclusively.

17. A method, comprising:

receiving a current input value from a plant or process during operation of the plant or process, and a desired output value;

predicting a plurality of input values at a plurality of intermediate times between a first time and a second time to define a dynamic operation path of the plant or process during operation of the plant or process between a current output value at the first time and the desired output value at the second time;

comparing a predicted dynamic output value from the dynamic operation path to the desired output value;

generating a primary error value as a difference between the predicted dynamic output value and the desired output value at each of the plurality of intermediate times;

determining a change in each input value to minimize the primary error value;

summing the determined change in each input value with an original input value to provide a future input value as a summed input value; and

controlling operation of an error minimization device to minimize the primary error value during operation of the plant or process in accordance with an objective of the controller to control operation of the plant or process.

18. The method of claim 17, wherein the error minimization device is operable to minimize an objective function of a dynamic predictive model with respect to a constraint.

19. The method of claim 18, wherein the error minimization device comprises an error constraint to determine operation of the error minimization device with respect to the constraint of the objective function during operation of the plant or process.

20. The method of claim 19, wherein the error minimization device is operable to minimize the objective function with respect to the constraint when the primary error value is above the error constraint, and wherein the error minimization device is not operable to minimize the objective function with respect to the constraint when the primary error value is below the error constraint.