Process control systems and methods having learning features

• US 8,909,359 B2
• Filed: 03/12/2013
• Issued: 12/09/2014
• Est. Priority Date: 05/10/2010
• Status: Active Grant

• Current Assignee: Tyco Fire & Security GmbH (Johnson Controls International plc)
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: Seem, John E.
• Primary Examiner(s): Bahta, Kidest
• Application Number:
  US13/797,806
• Publication Number:
  US 20130190900A1
• Time in Patent Office: 637 Days
• Field of Search: 700/83, 700/100.47, 700/45, 621/86, 624/15, 236/261, 236/13
• US Class Current: 700/45
• CPC Class Codes: F25B 49/027 Condenser control arrangements
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

A system for operating a process includes a processing circuit that uses a self-optimizing control strategy to learn a steady-state relationship between an input and an output. The processing circuit is configured to switch from using the self-optimizing control strategy to using a different control strategy that operates based on the learned steady-state relationship.

Claims

1. A system for operating a process, comprising:

a processing circuit that uses a self-optimizing control strategy to learn a steady state relationship between a manipulated variable and an output variable, wherein the self-optimizing control strategy adjusts the manipulated variable to achieve an extremum for the output variable and is executed by a first controller;

wherein the processing circuit is configured to automatically switch from using the self-optimizing control strategy to using a second control strategy that adjusts the manipulated variable using the learned steady state relationship, wherein the second control strategy is a model-based control strategy and is executed by a second controller different from the first controller.

2. The system of claim 1, wherein the second control strategy is an open loop control strategy that conducts open loop control based on the learned steady-state relationship.

3. The system of claim 1, wherein the second control strategy includes a feedback loop that operates based on the learned steady-state relationship.

4. The system of claim 1, wherein the processing circuit is configured to operate in the second control strategy normally and the self-optimizing control strategy periodically.

5. The system of claim 1, wherein the processing circuit is configured to operate the process according to the self-optimizing control strategy during a start-up state of the process.

6. The system of claim 5, wherein the processing circuit is configured to detect when a steady state has been reached after the start-up of the process.

7. The system of claim 6, wherein learning a steady-state relationship between the input and output comprises seeking a manipulated variable estimated to minimize energy consumption.

8. The system of claim 7, wherein the self-optimizing control strategy is an extremum seeking control strategy.

9. The system of claim 1, wherein the self-optimizing control strategy is an extremum seeking control strategy.

10. A computerized method for operating a process, comprising:

using a self-optimizing controller to learn a steady state relationship between a manipulated variable and an output variable;

using the self-optimizing controller to adjust the manipulated variable to achieve an extremum for the output variable; and

switching from using the self-optimizing controller to using a second controller that adjusts the manipulated variable based on the learned steady state relationship, wherein the second controller executes a model-based control strategy and is different from the self-optimizing controller.

11. The method of claim 10, wherein the second controller uses an open loop control strategy that conducts open loop control based on control variables observed in the learned steady state relationship.

12. The method of claim 10, further comprising:

operating the process using the second controller primarily and operating using the self-optimizing controller periodically.

13. The method of claim 10, further comprising:

operating the process using the self-optimizing controller during at least one of a start-up state and a training state of the process.

14. The method of claim 10, further comprising:

detecting whether a steady state has been obtained and learning the steady state relationship between a manipulated variable and an output variable by recording calculated and/or sensed parameters existing during the steady state relationship, the calculated and/or sensed parameters provided to the second controller for operation of a model-based control strategy.

15. The method of claim 10, wherein the self-optimizing controller is an extremum seeking controller.

16. A computer-readable storage device encoded with a computer program product, the computer program product including instructions that, when executed, perform operations comprising:

using a self-optimizing controller to learn a steady state relationship between a manipulated variable and an output variable of a process;

using the self-optimizing controller to adjust the manipulated variable to achieve an extremum for the output variable; and

automatically switching from using the self-optimizing controller to control the process to using a second controller for the process, wherein the second controller adjusts the manipulated variable based on the learned steady state relationship, wherein the second controller executes a model-based control strategy and is different from the self-optimizing controller.

17. The method of claim 16, wherein the second controller uses an open loop control strategy that conducts open loop control based on control variables observed in the learned steady state relationship.

18. The method of claim 16, wherein the second controller uses a closed loop feedback control strategy based on the control variables observed in the learned steady state relationship.

19. The method of claim 16, further comprising:

operating the process using the second controller primarily and periodically interrupting the second controller'"'"'s normal operation to use the self-optimizing controller for learning the steady state relationship.

20. The method of claim 19, further comprising:

operating the process using the self-optimizing controller during at least one of a start-up state and a training state of the process.

21. The method of claim 20, further comprising:

automatically detecting whether a steady state has been obtained and learning the steady state relationship between a manipulated variable and an output variable by recording calculated and/or sensed parameters existing during the steady state relationship, the calculated and/or sensed parameters provided to the second controller for operation of a model-based control strategy.