METHOD AND DEVICE FOR TUNING AND CONTROL

• US 20090138102A1
• Filed: 12/18/2006
• Published: 05/28/2009
• Est. Priority Date: 12/30/2005
• Status: Active Grant

• Current Assignee: ABB Schweiz AG (ABB Limited)
• Original Assignee: ABB AB (ABB Limited)
• Inventors: Moden, Per-Erik
• Granted Patent: US 7,937,165 B2
• Time in Patent Office: Days
• US Class Current: 700/37
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: Y10T 409/304648 with control means energized in response to activator stimulated by condition sensor
• CPC Class Codes: Y10T 409/304928 Adapted to hydraulically or pneumatically stimulate control
• CPC Class Codes: Y10T 409/304984 Adapted to electrically stimulate control
• CPC Class Codes: Y10T 409/30532 with means to advance work or product
• CPC Class Codes: Y10T 409/307224 with infeed control means energized in response to activator stimulated by condition sensor

Abstract

A method and a device for tuning and control of industrial processes having varying material flow rate. An adder is configured to add excitation signals to the controller output signal. A measurement system is configured to measure a property in response to the excitation signals. A model based tuning unit is adapted to estimate the value of at least one parameter with unknown value of a process model structure describing the effect of varying material flow rate, based on the measurements of the property and the output signal from the controller, and to calculate a model that describes the dynamics from controller output to controller input based on the estimated value of the parameter, and to perform model based tuning of the controller based on the model that describes the dynamics from controller output to controller input.

Claims

1. A method for tuning a controller controlling a property of an industrial process having varying material flow rate, the method comprising:

injecting excitation signals added to an output signal of the controller,

receiving measurements of said property in response to said excitation signals,

choosing a process model structure comprising at least one parameter with unknown value, this model structure describing an effect of varying material flow rate,

estimating a value of said at least one parameter, based on said measurements of said property and an output signal from the controller, and

calculating a model that describes dynamics from controller output to controller input based on the estimated value of said parameter, and on a basis thereof performing model based tuning of the controller.

2. The method according to claim 1, further comprising:

choosing a process model structure which is time continuous and comprises parameters being independent of varying material flow rate.

3. The method according to claim 1, further comprising:

choosing a process model structure comprising at most one model parameter per input/output pair being affected by varying actual gain of the process.

4. The method according to claim 1, further comprising:

choosing a process model structure comprising at most one model parameter per actuator being affected by varying actual response times of actuators.

5. The method according to claim 1, further comprising:

including, in the process model, any delays due to data processing and communication, and

choosing said process model structure such that variations of said delays do not influence more than one model parameter per model input.

6. The method according to claim 1, further comprising:

composing said process model structure of two parts, one part describing the transport behavior where possible deadtimes and possible time constants are inversely proportional to the material flow rate, and another part that is independent of the material flow rate.

7. The method according to claim 1, further comprising:

deriving a first order plus deadtime model for each part of said model,

introducing a transport behavior parameter that defines the degree of pure deadtime in relation to pure first order response of the transport behavior,

having, in the case of pure deadtime transport behavior, the deadtime described by the known distance,

having, in the case of pure first order transport behavior, the associated time constant described by the known distance, and

estimating at least one of the parameters gain, flow independent time constant, flow independent deadtime and transport behavior parameter.

8. The method according to claim 1, further comprising:

choosing the process model structure

$\frac{K<br><br><br><br>{\mathrm{<br><br>}}^{-D_0<br><br>s}}{1+\mathrm{\tau <br><br>}<br><br><br><br>s}<br><br>\frac{{\mathrm{<br><br>}}^{-\frac{L_d}{v}<br><br>\mathrm{\xi <br><br>}<br><br><br><br>s}}{1+\frac{L_m}{v}<br><br>\left(1-\mathrm{\xi <br><br>}\right)<br><br>s}$

wherein K represents the estimated parameters gain, D₀ represents flow independent deadtime, ξ

represents transport behavior parameter, L_d represents known distance describing the deadtime, and L_m represents the known distance describing the associated time constant.

9. The method according to claim 1, further comprising:

estimating at least one parameter using a prediction error method.

10. The method according to according to claim 1, further comprising:

reducing the parameter update size, when the unrestricted update reaches outside an area of defined allowed parameter ranges, but making the parameter update in the same direction in the parameter space,

applying the same reduction factor to both regressors and prediction error, to be used in both parameter update and update of matrix holding information necessary in recursive estimation, and

thereby keeping parameter estimates within allowed ranges.

11. The method according to claim 1, further comprising:

reducing, for the case of large prediction errors, the effect of measurements and regressors according to a belief factor calculated from the ratio between prediction error size and estimated standard deviation of previous prediction errors, and

thereby achieving protection against outliers.

12. The method according to claim 1, further comprising:

sampling process outputs at a basic sampling rate, applying a filter with the same sampling rate, and re-sampling to a slower sampling rate, this rate being used for said logging of values from operation of the process, and including the effect of this sampling, filtering and re-sampling in the prediction calculations and parameter estimate updates.

13. The method according to claim 1, further comprising:

performing an identification experiment meaning operating the process with intentional excitation influencing at least one manipulated variable, and

estimating at least one parameter on-line during said identification experiment.

14. The method according to claim 1, further comprising:

calculating said model based tuning of the controller on-line.

15. The method according to claim 1, further comprising:

presenting results to a user in real-time, thereby allowing user decision to end the identification experiment as soon as satisfactory results have been obtained.

16. The method according to claim 1, further comprising:

estimating at least one parameter on-line during operation of the process and while controlling it with said controller,

performing said model based tuning calculations for the controller on-line, and

applying the tuning result to said controller on-line,

thereby achieving true adaptive control.

17. The method according to claim 1, further comprising:

estimating fewer parameters during operation with on-line updating of the controller than during the identification experiment, and

allowing the parameter estimation result from the identification experiment to define the value of at least one parameter, and treating this parameter or these parameters as known during the operation with on-line updating of the controller.

18. The method according to claim 1, further comprising:

using lambda tuning or IMC tuning as model based tuning method for said controller.

19. A device for tuning a controller controlling a property of an industrial process having varying material flow rate, the device comprising:

an adder for adding excitation signals to a controller output signal,

a measurement system for measuring said property in response to said excitation signals, and

a model based tuning unit adapted to estimate a value of at least one parameter with unknown value of a process model structure describing the effect of varying material flow rate, based on said measurements of said property and the output signal from the controller, and to calculate a model that describes the dynamics from controller output to controller input based on the estimated value of said parameter, and to perform model based tuning of the controller based on said model that describes the dynamics from controller output to controller input.

20. The device according to claim 19, wherein said process model structure is time continuous and comprises parameters being independent of varying material flow rate.

21. A computer program product, comprising:

a computer readable medium; and

computer program instructions recorded on the computer readable medium and executable by a processor for carrying out a method for tuning a controller controlling a property of an industrial process having varying material flow rate, the method comprising;

injecting excitation signals added to an output signal of the controller,

receiving measurements of said property in response to said excitation signals,

choosing a process model structure comprising at least one parameter with unknown value, this model structure describing an effect of varying material flow rate,

estimating a value of said at least one parameter, based on said measurements of said property and an output signal from the controller, and

calculating a model that describes dynamics from controller output to controller input based on the estimated value of said parameter, and on a basis thereof performing model based tuning of the controller.

22. (canceled)