Method for implementing indirect controller

• US 20050216103A1
• Filed: 02/02/2005
• Published: 09/29/2005
• Est. Priority Date: 06/05/2003
• Status: Active Grant

• Current Assignee: GE Digital Holdings LLC (General Electric Co. (Italy))
• Original Assignee: NeuCo Incorporated (General Electric Company)
• Inventors: Lefebvre, Wesley Curt, Kohn, Daniel W.
• Granted Patent: US 7,164,954 B2
• Time in Patent Office: Days
• US Class Current: 700/29
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G05B 7/02 electric
• CPC Class Codes: G06F 18/24133 Distances to prototypes
• CPC Class Codes: G06V 10/82 using neural networks
• CPC Class Codes: Y10S 706/903 Control
• CPC Class Codes: Y10S 706/906 Process plant
• CPC Class Codes: Y10S 706/914 Process plant

Abstract

A system and method for implementing an indirect controller for a plant. A plant can be provided with both a direct controller and an indirect controller with a system model or a committee of system models. When the system model has sufficient integrity to satisfy the plant requirements, i.e., when the system model has been sufficiently trained, the indirect controller with the system model is automatically enabled to replace the direct controller. When the performance falls, the direct controller can automatically assume operation of the plant, preferably maintaining operation in a control region suitable for generating additional training data for the system model. Alternatively, the system model incorporates a committee of models. Various types of sources for errors in the committee of models can be detected and used to implement strategies to improve the quality of the committee.

Claims

1. A computer-implemented method for implementing an indirect controller with a committee of system models in a plant operable for performing a process, the plant having a plurality of inputs, the inputs being adjustable according to control settings received from a controller, comprising:

selecting the indirect controller to operate the plant;

obtaining a set of possible control settings from each system model in the committee that is operative;

evaluating confidence in the committee of models using the sets of possible control settings; and

if the confidence in the committee is acceptable, generating a set of desired control settings from the sets of possible control settings.

2. The method of claim 1, wherein evaluating confidence in the sets of possible control settings includes determining a confidence interval for the sets of possible control settings.

3. The method of claim 2, wherein evaluating confidence in the sets of possible control settings includes determining a confidence ratio for the sets of possible control settings and comparing the confidence ratio to a threshold.

4. The method of claim 1, if the confidence in the committee is not acceptable, further comprising selecting an alternate controller to operate the plant.

5. The method of claim 4, further comprising adapting the system models in the committee and shifting control to the direct controller when the performance of the committee is not acceptable.

6. The method of claim 5, wherein adapting the system models in the committee includes generating a new system model and including it in the committee.

7. The method of claim 4, wherein the alternate controller is a direct controller.

8. The method of claim 7, further including the direct controller operating the plant around an operating region corresponding to where the confidence in the committee is not acceptable.

9. The method of claim 8, further comprising adapting the system models in the committee and shifting control to the indirect controller when the performance of the committee is acceptable.

10. The method of claim 1, wherein generating a set of desired control settings includes combining the sets of possible control settings.

11. The method of claim 10, wherein combining the sets of possible control settings includes averaging the sets of possible control settings.

12. The method of claim 1, wherein fuzzy logic is used to determine whether the confidence is acceptable or not acceptable.

13. The method of claim 1, wherein at least one of the system models in the committee is a neural network.

14. The method of claim 1, wherein at least one of the system models in the committee is a genetically programmed model.

15. The method of claim 1, further comprising, if the confidence in the committee is not acceptable, detecting whether the system models in the committee have systematic errors or random errors and implementing a strategy for correction dependent on whether systematic errors or random errors are detected.

16. The method of claim 1, further comprising selecting a subset of the system models in the committee to be operative, the subset comprising the system models in the committee having the best performance.

17. A computer program product, residing on a computer readable medium, for use in implementing an indirect controller with a committee of system models in a plant operable for performing a process, the plant having a plurality of inputs, the inputs being adjustable according to control settings received from a controller, the computer program product comprising instructions for causing a computer to:

select the indirect controller to operate the plant;

obtain a set of possible control settings from each system model in the committee that is operative;

evaluate confidence in the committee of models using the set of possible control settings; and

if the confidence in the committee is acceptable, generate a set of desired control settings from the set of possible control settings.

18. The computer program product of claim 17, wherein the instructions cause the computer to determine a confidence interval for the sets of possible control settings.

19. The computer program product of claim 18, wherein the instructions cause the computer to determine a confidence ratio for the sets of possible control settings.

20. The computer program product of claim 17, further comprising instructions for causing the computer to, if the confidence in the committee is not acceptable, select an alternate controller to operate the plant.

21. The computer program product of claim 20, further comprising instructions for causing the computer to instruct the alternate controller to operate the plant around an operating region corresponding to where the confidence in the committee is not acceptable.

22. The computer program product of claim 18, further comprising instructions for causing the computer to adapt the system models in the committee and shift control to the indirect controller when the performance of the committee is acceptable.

23. The computer program product of claim 17, further comprising instructions for causing the computer to combine the sets of possible control settings to generate the set of desired control settings.

24. The computer program product of claim 21, further comprising instructions for causing the computer to average the sets of possible control settings.

25. The computer program product of claim 21, further comprising instructions for causing the computer to detect whether the system models in the committee have systematic errors or random errors and implement a strategy for correction dependent on whether systematic errors or random errors are detected.

26. A system for implementing indirect control in a plant operable for performing a process, the plant having a plurality of inputs, the inputs being adjustable according to control settings received from a means for control, comprising:

means for modeling the operation of the plant to generate a plurality of sets of possible control settings;

means for evaluating confidence in the plurality of sets of possible control settings;

means for providing indirect control to operate the plant using the sets of possible control settings;

means for providing direct control to operate the plant; and

means for selecting indirect control or direct control to operate the plant.