Combined proportional plus integral (PI) and neural network (nN) controller

• US 20030055798A1
• Filed: 09/09/2002
• Published: 03/20/2003
• Est. Priority Date: 09/08/2001
• Status: Active Grant

• Current Assignee: Colorado State University Research Foundation
• Original Assignee: Colorado State University Research Foundation
• Inventors: Anderson, Michael, Anderson, Charles, Hittle, Douglas C., Young, Peter M., Delnero, Christopher
• Granted Patent: US 7,117,045 B2
• Time in Patent Office: Days
• US Class Current: 706/15
• CPC Class Codes: G05B 11/42 for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: Y10S 706/906 Process plant

Abstract

A neural network controller in parallel with a proportional-plus-integral (PI) feedback controller in a control system. At least one input port of the neural network for receiving an input signal representing a condition of a process is included. A first set of data is obtained that includes a plurality of output values of the neural network obtained during a training period thereof using a plurality of first inputs representing a plurality of conditions of the process. The process/plant condition signals generally define the process/plant, and may include one set-point as well as signals generated using measured systems variables/parameters. In operation, the neural network contributes to an output of the PI controller only upon detection of at least one triggering event, at which time a value of the first set of data corresponding with the condition deviation is added-in thus, contributing to the proportional-plus-integral feedback controller. The triggering event can be characterized as (a) a change in any one of the input signals greater-than a preselected amount, or (b) a detectable process condition deviation greater-than a preselected magnitude, for which an adjustment is needed to the process/plant being controlled. Also a method for controlling a process with a neural network controller operating in parallel with a IP controller is included.

Claims

1. A neural network controller in parallel with a proportional-plus-integral feedback controller in a control system, the system comprising:

at least one input port of the neural network for receiving an input signal representing a condition of a process;

a first set of data comprising a plurality of output values of the neural network obtained during a training period thereof using a plurality of first inputs representing a plurality of conditions of said process; and

in operation, the neural network to contribute to an output of the proportional-plus-integral feedback controller only upon detection of at least one triggering event connected with said input signal, at which time a value of said first set of data corresponding with said condition deviation so contributes to the proportional-plus-integral feedback controller.

2. The system of claim 1 further comprising a second input port for receiving a second input signal representing a second condition of said process;

and wherein said first set of data was obtained earlier than said operation of the neural network, and said triggering event comprises a change in any one of said input signals greater-than a preselected amount.

3. The system of claim 2 wherein said plurality of first inputs comprises real input information about said process, said change is caused by a disturbance of said process, and said preselected amount comprises a fraction of a prediction value from said first set of data corresponding to a respective of said plurality of first inputs, said fraction selected from a range comprising from 1% to 5%.

4. The system of claim 2 wherein at least one of said input signals represents a condition set-point, said change is caused by an alteration of said condition set-point, and said preselected amount comprises a fraction of a prediction value from said first set of data corresponding to a respective of said plurality of first inputs comprising said input signal for said altered condition set-point.

5. The system of claim 4 wherein said alternation is a manual alteration of said condition set-point, said plurality of first inputs comprises real input information about said process, and said fraction is selected from a range comprising from 1% to 5%, and wherein and said change is a result of a detectable process condition deviation.

6. The system of claim 1 wherein said value of said first set of data, ONN, corresponding with said condition deviation is added-in to the proportional-plus-integral feedback controller according to a discrete form of the proportional-plus-integral feedback controller expression:

7. The system of claim 6 in which said output value, O_τ

, derived by said addition of said value of said first set of data, ONN, to the proportional-plus-integral feedback controller, is used as a process input for said process; and

wherein and said triggering event comprises a detectable process condition deviation greater-than a preselected magnitude.

8. The system of claim 1 further comprising second, third, and fourth input ports for receiving, respectively, second, third, and fourth input signals representing a second, third, and fourth condition of said process;

and wherein said triggering event comprises a change in any one of said input signals greater-than a preselected amount, said preselected amount comprising a fraction of a prediction value from said first set of data corresponding to a respective of said plurality of first inputs.

9. The system of claim 8 wherein the neural network controller comprises a feed forward controller, said plurality of first inputs comprises real input information about said process, said first set of data being obtained on-line during said operation of the neural network, said fraction selected from a range comprising from 1% to 5%.

10. The system of claim 8 wherein the neural network controller comprises a feed forward controller, said plurality of first inputs comprises simulated input information about said process, said first set of data was obtained earlier, off-line, from said operation of the neural network, and wherein and said change is a result of a detectable process condition deviation.

11. A neural network controller in parallel with a proportional-plus-integral feedback controller in a control system, the system comprising:

a plurality of input ports of the neural network, each said input port for receiving a respective input signal representing a respective condition of a process;

a first set of data comprising a plurality of output values of the neural network obtained during a training period thereof using a plurality of first inputs representing a plurality of conditions of said process; and

in operation, the neural network to contribute to an output of the proportional-plus-integral feedback controller only upon detection of at least one triggering event, said event comprising a change in any one of said respective input signals greater-than a preselected amount, indicating a condition deviation.

12. The system of claim 11 wherein said plurality of first inputs comprises real input information about said process, said change is caused by a disturbance of said process, and said preselected amount comprises a fraction of a prediction value from said first set of data corresponding to a respective of said plurality of first inputs, said fraction selected from a range comprising from 1% to 5%.

13. The system of claim 11 wherein the neural network controller comprises a feed forward controller, at least one of said respective input signals represents a condition set-point, said change is caused by an alteration of said condition set-point, and said plurality of first inputs comprises simulated input information about said process.

14. The system of claim 11 wherein upon said detection, a value of said first set of data, ONN, corresponding with said condition deviation is added-in to the proportional-plus-integral feedback controller according to a discrete form of the proportional-plus-integral feedback controller expression:

15. A method for controlling a process with a neural network controller operating in parallel with a proportional-plus-integral feedback controller, the method comprising the steps of:

generating a first set of data comprising a plurality of output values of the neural network obtained during a training period thereof using a plurality of first inputs representing a plurality of conditions of a process;

receiving, at each of a plurality of input ports of the neural network, an input signal representing a respective condition of said process; and

the neural network to contribute to an output of the proportional-plus-integral feedback controller only upon detection of at least one triggering event, said triggering event comprising a change in any one of said respective input signals greater-than a preselected amount.

16. The method of claim 15 wherein said step of generating further comprises using real input information about said process for said plurality of first inputs;

said change is caused by a disturbance of said process; and

upon said detection, said contribution to said output comprises adding-in a value of said first set of data corresponding with said condition deviation to the proportional-plus-integral feedback controller.

17. The method of claim 15 wherein said step of generating further comprises using simulated input information about said process for said plurality of first inputs;

said receiving further comprises at least one of said input signals representing a condition set-point;

said change is caused by an alternation of said condition set-point; and

said triggering event further comprises said preselected amount comprising a fraction of a prediction value from said first set of data corresponding to a respective of said plurality of first inputs comprising said input signal for said altered condition set-point.

18. The method of claim 15 wherein:

said training period is substantially completed prior to said step of receiving said input signals in connection with controlling said process;

said triggering event further comprises said preselected amount comprising a fraction of a prediction value from said first set of data corresponding to a respective of said plurality of first inputs, said fraction selected from a range comprising from 1% to 5%; and

upon said detection, said contribution to said output comprises adding-in a value of said first set of data corresponding with said condition deviation to the proportional-plus-integral feedback controller.

19. The method of claim 15 wherein the neural network controller comprises a feed forward controller, and upon said detection, said contribution to said output comprises adding-in a value of said first set of data, ONN, corresponding with said condition deviation to the proportional-plus-integral feedback controller according to a discrete form of the proportional-plus-integral feedback controller expression:

20. The method of claim 19 wherein:

said training period takes place at least on-line and during said step of receiving said input signals in connection with controlling said process; and

said output value, O_τ

, derived by said adding said value of said first set of data, ONN, to the proportional-plus-integral feedback controller, is used as a process input for said process.