METHOD AND APPARATUS OF A SELF-CONFIGURED, MODEL-BASED ADAPTIVE, PREDICTIVE CONTROLLER FOR MULTI-ZONE REGULATION SYSTEMS

• US 20110022193A1
• Filed: 04/08/2010
• Published: 01/27/2011
• Est. Priority Date: 07/27/2009
• Status: Abandoned Application

• Current Assignee: Siemens Industry Incorporated (Siemens AG)
• Original Assignee: Siemens Industry Incorporated (Siemens AG)
• Inventors: Panaitescu, Razvan
• Application Number:
  US12/756,279
• Publication Number:
  US 20110022193A1
• Time in Patent Office: Days
• US Class Current: 700/29
• CPC Class Codes: A41D 13/087 especially for fingers
• CPC Class Codes: A41D 20/00 Wristbands or headbands, e.g. for absorbing sweat
• CPC Class Codes: A41D 27/205 Pockets adapted to receive a mobile phone or other electronic equipment
• CPC Class Codes: A45C 2200/10 Transparent walls
• CPC Class Codes: A45F 2005/008 Hand articles fastened to the wrist or to the arm or to the leg
• CPC Class Codes: A45F 2200/0508 Portable audio devices, e.g. walkman, discman, radio, MP3 player, headphones
• CPC Class Codes: A45F 5/00 Holders or carriers for hand articles; Holders or carriers for use while travelling or camping

Abstract

A control system simultaneously controls a multi-zone process with a self-adaptive model predictive controller (MPC), such as temperature control within a plastic injection molding system. The controller is initialized with basic system information. A pre-identification procedure determines a suggested system sampling rate, delays or “dead times” for each zone and initial system model matrix coefficients necessary for operation of the control predictions. The recursive least squares based system model update, control variable predictions and calculations of the control horizon values are preferably executed in real time by using matrix calculation basic functions implemented and optimized for being used in a S7 environment by a Siemens PLC. The number of predictions and the horizon of the control steps required to achieve the setpoint are significantly high to achieve smooth and robust control. Several matrix calculations, including an inverse matrix procedure performed at each sample pulse and for each individual zone determine the MPC gain matrices needed to bring the system with minimum control effort and variations to the final setpoint. Corrective signals, based on the predictive model and the minimization criteria explained above, are issued to adjust system heating/cooling outputs at the next sample time occurrence, so as to bring the system to the desired set point. The process is repeated continuously at each sample pulse.

Claims

1. A model-based adaptive, predictive process control system comprising:

a multi-zoned regulated apparatus having in each zone at least one input field device that measures a controlled parameter and at least one output field device capable of influencing the controlled parameter;

a controller coupled to the respective field devices and performing the following operational steps in at least a plurality of the zones in real time;

periodically sampling the controlled parameter with the respective input field device and predictively modeling future sample readings thereof in a moving window prediction model horizon;

accumulating a plurality of the moving window prediction model horizons and periodically, in a moving window control horizon, predictively modeling future controlled apparatus response of the plurality of zones as a function of the accumulated prediction model horizons;

comparing the modeled response of the controlled apparatus in the control horizon to a desired response;

adjusting the system model based on past measurements;

adjusting the respective output field device in order to attempt to converge modeled and desired responses; and

periodically repeating the above controlled functions.

2. The system of claim 1, wherein prior to the sampling step the controller performs an initialization step comprising:

receiving operational parameter inputs selected from the group consisting of;

number of zones, sampling rates for horizons, type of controlled parameter, anticipated system zone response speed to controlled parameter changes, system boundary and operational limits and system initial conditions.

3. The system of claim 2 further comprising initialization steps of:

exciting simultaneously the field output devices of all of the plurality of zones with an identification excitation signal and sampling the respective field input devices to identify system response parameters for the predictive modeling horizon window sampling;

approximating overall system response sampling rates for at least one of the horizons;

determining system response delays to field output device adjustments for each of the respective zones; and

preparing model predictive matrices in the controller for performing the modeling steps, and initializing matrix coefficients for system modeling parameters.

4. The system of claim 3, wherein the identification excitation signal is a pseudo random binary signal (PRBS) with amplitudes varying between 0% and 100% simultaneously applied to all of the plurality of zones.

5. The system of claim 1, wherein the controlled parameter is selected from the group consisting of current, voltage, temperature, humidity, pressure, flow rate and manufactured product specifications.

6. The system of claim 5, wherein the controlled parameter is temperature and the controlled system is selected from the group consisting of plastic molding machines, and heating ventilating and air conditioning (HVAC) systems.

7. The system of claim 6, wherein at least one zone output field devices are coupled to respective heating and cooling elements that operate with opposed output offsets and system temperature in said zone is regulated by increasing output of one of the elements while simultaneously maintaining a small bias value of the other opposing element.

8. The system of claim 1, wherein modeling is performed by the controller with mathematical matrix operations, matrix dimensions established by the respective numbers of steps in the horizons, with the modeled response matrix operations utilizing predictive model matrices;

and the models update matrix coefficients during subsequent repetitive modeling steps in future horizon modeling.

9. The system of claim 8, wherein the system model has matrix coefficients that are updated using recursive least squares computational methods.

10. The system of claim 9, wherein the output field device adjustments are modified by differential adjustment of the existing adjustment stored within the controller.

11. A model-based adaptive, predictive process controller capable of being coupled to at least one input field device that measures a controlled parameter and at least one output field device capable of influencing the controlled parameter for each respective zone of a multi-zoned regulated apparatus, the controller capable of performing the following operational steps in at least a plurality of the zones in real time:

periodically sampling the controlled parameter with the respective input field device and predictively modeling future sample readings thereof in a moving window prediction model horizon;

accumulating a plurality of the moving window prediction model horizons and periodically, in a moving window control horizon, predictively modeling future controlled apparatus response of the plurality of zones as a function of the accumulated prediction model horizons;

comparing the modeled response of the controlled apparatus in the control horizon to a desired response;

adjusting the system model based on past measurements;

adjusting the respective output field device in order to attempt to converge modeled and desired responses; and

periodically repeating the above controlled functions.

12. The controller of claim 11, wherein prior to the sampling step the controller performs an initialization step comprising:

receiving operational parameter inputs selected from the group consisting of;

number of zones, sampling rates for horizons, type of controlled parameter, anticipated system zone response speed to controlled parameter changes, system boundary and operational limits and system initial conditions.

13. The controller of claim 12 further comprising initialization steps of:

exciting simultaneously the field output devices of all of the plurality of zones with an identification excitation signal and sampling the respective field input devices to identify system response parameters for the predictive modeling horizon window sampling;

approximating overall system response sampling rates for at least one of the horizons;

determining system response delays to field output device adjustments for each of the respective zones; and

preparing model predictive matrices in the controller for performing the modeling steps, and initializing matrix coefficients for system modeling parameters.

14. The controller of claim 13, wherein the identification excitation signal is a pseudo random binary signal (PRBS) with amplitudes varying between 0% and 100% simultaneously applied to all of the plurality of zones.

15. The controller of claim 11, wherein the controlled parameter is selected from the group consisting of current, voltage, temperature, humidity, pressure flow rate and manufactured product specifications.

16. The controller of claim 15, wherein the controlled parameter is temperature and the controlled system is selected from the group consisting of plastic molding machines, and heating ventilating and air conditioning (HVAC) systems.

17. The controller of claim 16, wherein at least one zone output field devices are coupled to respective heating and cooling elements that operate with opposed output offsets and system temperature in said zone is regulated by increasing output of one of the elements while simultaneously maintaining a small bias value of the other opposing element.

18. The controller of claim 11, wherein modeling is performed by the controller with mathematical matrix operations, matrix dimensions established by the respective numbers of steps in the horizons, with the modeled response matrix operations utilizing predictive model matrices;

and the models update matrix coefficients during subsequent repetitive modeling steps in future horizon modeling.

19. The controller of claim 18, wherein the system model has matrix coefficients that are updated using recursive least squares computational methods.

20. The controller of claim 19, wherein the output field device adjustments are modified by differential adjustment of the existing adjustment stored within the controller.

21. In a process control system including a multi-zoned regulated apparatus having in each zone at least one input field device that measures a controlled parameter and at least one output field device capable of influencing the controlled parameter, and a controller coupled to the respective field devices, a method for operating the controller in real time, comprising the steps of:

periodically sampling the controlled parameter with the respective input field device and predictively modeling future sample readings thereof in a moving window prediction model horizon;

accumulating a plurality of the moving window prediction model horizons and periodically, in a moving window control horizon, predictively modeling future controlled apparatus response of the plurality of zones as a function of the accumulated prediction model horizons;

comparing the modeled response of the controlled apparatus in the control horizon to a desired response;

adjusting the system model based on past measurements;

adjusting the respective output field device in order to attempt to converge modeled and desired responses; and

periodically repeating the above controlled functions.

22. The method of claim 21, wherein prior to the sampling step the controller performs an initialization step comprising:

receiving operational parameter inputs selected from the group consisting of;

number of zones, sampling rates for horizons, type of controlled parameter, anticipated system zone response speed to controlled parameter changes, system boundary and operational limits and system initial conditions.

23. The method of claim 22 further comprising initialization steps of:

exciting simultaneously the field output devices of all of the plurality of zones with an identification excitation signal and sampling the respective field input devices to identify system response parameters for the predictive modeling horizon window sampling;

approximating overall system response sampling rates for at least one of the horizons;

determining system response delays to field output device adjustments for each of the respective zones; and

preparing model predictive matrices in the controller for performing the modeling steps, and initializing matrix coefficients for system modeling parameters.

24. The method of claim 23, wherein the identification excitation signal is a pseudo random binary signal (PRBS) with amplitudes varying between 0% and 100% simultaneously applied to all of the plurality of zones.

25. The method of claim 21, wherein the controlled parameter is selected from the group consisting of current, voltage, temperature, humidity, pressure flow rate and manufactured product specifications.

26. The method of claim 25, wherein the controlled parameter is temperature and the controlled system is selected from the group consisting of plastic molding machines, and heating ventilating and air conditioning (HVAC) systems.

27. The method of claim 26, wherein at least one zone output field devices are coupled to respective heating and cooling elements that operate with opposed output offsets and system temperature in said zone is regulated by increasing output of one of the elements while simultaneously maintaining a small bias value of the other opposing element.

28. The method of claim 21, wherein modeling is performed by the controller with mathematical matrix operations, matrix dimensions established by the respective numbers of steps in the horizons, with the modeled response matrix operations utilizing predictive model matrices;

and the models update matrix coefficients during subsequent repetitive modeling steps in future horizon modeling.

29. The method of claim 28, wherein the system model has matrix coefficients that are updated using recursive least squares computational methods.

30. The method of claim 29, wherein the output field device adjustments are modified by differential adjustment of the existing adjustment stored within the controller.

31. A storage medium comprising model-based adaptive, predictive process controller software capable of execution by a processor within a process controller, wherein the controller is in turn coupled to at least one input field device that measures a controlled parameter and at least one output field device capable of influencing the controlled parameter for each respective zone of a multi-zoned regulated apparatus, the software when executed by the processor causing the controller to perform the following operational steps in at least a plurality of the zones in real time:

periodically sampling the controlled parameter with the respective input field device and predictively modeling future sample readings thereof in a moving window prediction model horizon;

accumulating a plurality of the moving window prediction model horizons and periodically, in a moving window control horizon, predictively modeling future controlled apparatus response of the plurality of zones as a function of the accumulated prediction model horizons;

comparing the modeled response of the controlled apparatus in the control horizon to a desired response;

adjusting the system model based on past measurements;

adjusting the respective output field device in order to attempt to converge modeled and desired responses; and

periodically repeating the above controlled functions.

32. A model-based adaptive, predictive process controller capable of being coupled to at least one input field device that measures a controlled parameter and at least one output field device capable of influencing the controlled parameter for each respective zone of a multi-zoned regulated apparatus, the controller having means for performing the following operational steps in at least a plurality of the zones in real time:

periodically sampling the controlled parameter with the respective input field device and predictively modeling future sample readings thereof in a moving window prediction model horizon;

accumulating a plurality of the moving window prediction model horizons and periodically, in a moving window control horizon, predictively modeling future controlled apparatus response of the plurality of zones as a function of the accumulated prediction model horizons;

comparing the modeled response of the controlled apparatus in the control horizon to a desired response;

adjusting the system model based on past measurements;

adjusting the respective output field device in order to attempt to converge modeled and desired responses; and

periodically repeating the above controlled functions.