Method and apparatus for designing model-based control having spatial robustness for multiple array cross-direction (CD) web manufacturing or processing systems or other systems

Patent metadata

Field	Value
Publication number	US-10890882-B2
Application number	US-201815889005-A
Country	US
Kind code	B2
Filing date	Feb 5, 2018
Priority date	Mar 6, 2017
Publication date	Jan 12, 2021
Grant date	Jan 12, 2021

Abstract

A method includes obtaining one or more models associated with a model-based controller in an industrial process having multiple actuator arrays and performing spatial tuning of the controller. The spatial tuning includes identifying weighting matrices that suppress one or more frequency components in actuator profiles of the actuator arrays. The spatial tuning could also include finding a worst-case cutoff frequency over all output channels for each process input, designing the weighting matrices to penalize higher-frequency actuator variability based on the model(s) and the cutoff frequencies, and finding a multiplier for a spatial frequency weighted actuator variability term in a function that guarantees robust spatial stability. The controller could be configured to use a function during control of the industrial process, where a change to one or more terms of the function alters operation of the controller and the industrial process and at least one term is based on the weighting matrices.

First claim

What is claimed is:

1. A method comprising:
obtaining one or more models associated with a model-based controller in an industrial process having multiple actuator arrays; and
performing spatial tuning of the controller, wherein the spatial tuning comprises identifying weighting matrices that suppress one or more frequency components in actuator profiles of the actuator arrays, wherein the weighting matrices are based on bending and picketing, the weighted matrices are further based on scalar weights and a bending moment matrix with dimension for each actuator profile.

2. The method of claim 1 , further comprising:
performing temporal tuning of the controller.

3. The method of claim 1 , wherein each of the one or more models is represented in a two-dimensional frequency domain.

4. The method of claim 1 , wherein the spatial tuning comprises using the weighting matrices to shape a spatial spectrum of each of the actuator profiles.

5. The method of claim 4 , wherein the spatial tuning further comprises adjusting the weighting matrices based on user-specific parametric uncertainty.

6. The method of claim 1 , wherein the spatial tuning further comprises:
for each process input, finding a worst-case cutoff frequency over all output channels considering the one or more models, given parametric uncertainty;
based on the one or more models and the worst-case cutoff frequencies, designing the weighting matrices to penalize higher-frequency actuator variability; and
finding a multiplier for a spatial frequency weighted actuator variability term in a cost function that guarantees robust spatial stability.

7. The method of claim 1 , wherein the spatial tuning further comprises:
providing one or more terms of a cost function to the model-based controller, the model-based controller configured to use the cost function during control of the industrial process such that a change to the one or more terms of the cost function alters operation of the model-based controller and the industrial process, at least one of the one or more terms based on the weighting matrices.

8. An apparatus comprising:
at least one memory configured to store one or more models associated with a model-based controller in an industrial process having multiple actuator arrays; and
at least one processing device configured to perform spatial tuning of the controller;
wherein, to perform the spatial tuning, the at least one processing device is configured to identify weighting matrices that suppress one or more frequency components in actuator profiles of the actuator arrays, wherein the weighting matrices are based on bending and picketing, the weighted matrices are further based on scalar weights and a bending moment matrix with dimension for each actuator profile.

9. The apparatus of claim 8 , wherein the at least one processing device is further configured to perform temporal tuning of the controller.

10. The apparatus of claim 8 , wherein each of the one or more models is represented in a two-dimensional frequency domain.

11. The apparatus of claim 8 , wherein, to perform the spatial tuning, the at least one processing device is configured to use the weighting matrices to shape a spatial spectrum of each of the actuator profiles.

12. The apparatus of claim 11 , wherein, to perform the spatial tuning, the at least one processing device is configured to adjust the weighting matrices based on user-specific parametric uncertainty.

13. The apparatus of claim 8 , wherein, to perform the spatial tuning, the at least one processing device is further configured to:
for each process input, find a worst-case cutoff frequency over all output channels considering the one or more models, given parametric uncertainty;
based on the one or more models and the worst-case cutoff frequencies, design the weighting matrices to penalize higher-frequency actuator variability; and
find a multiplier for a spatial frequency weighted actuator variability term in a cost function that guarantees robust spatial stability.

14. The apparatus of claim 8 , wherein the at least one processing device is further configured to provide one or more terms of a cost function to the model-based controller, the model-based controller configured to use the cost function during control of the industrial process such that a change to the one or more terms of the cost function alters operation of the model-based controller and the industrial process, at least one of the one or more terms based on the weighting matrices.

15. A non-transitory computer readable medium containing instructions that when executed cause at least one processing device to:
obtain one or more models associated with a model-based controller in an industrial process having multiple actuator arrays; and
perform spatial tuning of the controller, wherein the spatial tuning comprises identifying weighting matrices that suppress one or more frequency components in actuator profiles of the actuator arrays, wherein the weighting matrices are based on bending and picketing, the weighted matrices are further based on scalar weights and a bending moment matrix with dimension for each actuator profile.

16. The non-transitory computer readable medium of claim 15 , further containing instructions that when executed cause the at least one processing device to perform temporal tuning of the controller.

17. The non-transitory computer readable medium of claim 15 , wherein the instructions that when executed cause the at least one processing device to perform the spatial tuning comprise:
instructions that when executed cause the at least one processing device to use the weighting matrices to shape a spatial spectrum of each of the actuator profiles.

18. The non-transitory computer readable medium of claim 17 , wherein the instructions that when executed cause the at least one processing device to perform the spatial tuning comprise:
instructions that when executed cause the at least one processing device to adjust the weighting matrices based on user-specific parametric uncertainty.

19. The non-transitory computer readable medium of claim 15 , wherein the instructions that when executed cause the at least one processing device to perform the spatial tuning comprise:
instructions that when executed cause the at least one processing device to:
for each process input, find a worst-case cutoff frequency over all output channels considering the one or more models, given parametric uncertainty;
based on the one or more models and the worst-case cutoff frequencies, design the weighting matrices to penalize higher-frequency actuator variability; and
find a multiplier for a spatial frequency weighted actuator variability term in a cost function that guarantees robust spatial stability.

20. The non-transitory computer readable medium of claim 15 , further containing instructions that when executed cause the at least one processing device to:
provide one or more terms of a cost function to the model-based controller, the model-based controller configured to use the cost function during control of the industrial process such that a change to the one or more terms of the cost function alters operation of the model-based controller and the industrial process, at least one of the one or more terms based on the weighting matrices.

Assignees

• Honeywell Ltd
• Honeywell Int Inc

Inventors

• He Ning
• Forbes Michael
• Backstrom Johan U
• Chen Tongwen

Classifications

• Y02P90/02

  Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS] · CPC title

• G06Q50/04

  Manufacturing · CPC title

• G05B19/41885

  characterised by modeling, simulation of the manufacturing system · CPC title

• G05B2219/42155

  Model · CPC title

• G06Q10/067

  Enterprise or organisation modelling · CPC title