Integrated model predictive control and optimization within a process control system

• US 20040049299A1
• Filed: 09/11/2002
• Published: 03/11/2004
• Est. Priority Date: 09/11/2002
• Status: Active Grant

• Current Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Original Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Inventors: Nixon, Mark, Wojsznis, Wilhelm, Blevins, Terry
• Granted Patent: US 7,376,472 B2
• Time in Patent Office: Days
• US Class Current: 700/29
• CPC Class Codes: G05B 11/32 with inputs from more than one sensing element; with outputs to more than one correcting element
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 13/048 using a predictor

Abstract

An integrated optimization and control technique integrates an optimization procedure, such as a linear or quadratic programming optimization procedure, with advanced control, such as model predictive control, within a process plant in which the number of control and auxiliary variables can be greater than the number of manipulated variables within the process plant. The technique first determines a step response matrix defining the correlation between changes in the manipulated variables and each of the process variables that are used during optimization. A subset of the control variables and auxiliary variables is then selected to be used as inputs to a model predictive control routine used to perform control during operation of the process and a square M by M control matrix to be used by the model predictive control routine is generated. Thereafter, during each scan of the process controller, the optimizer routine calculates the optimal operating target of each of the complete set of control and auxiliary variables and provides the determined target operating points for each of the selected subset of control and auxiliary variables to the model predictive control routine as inputs. The model predictive control routine determines changes in the manipulated variables for use in controlling the process from the target and measured values for each of the subset of the control and auxiliary variables and the M by M control matrix.

Claims

1. A process control system for controlling a process, comprising:

a multiple-input/multiple-output controller adapted to produce, during each operational cycle of the process control system, multiple control outputs configured to control the process based on multiple measured inputs from the process and based on a set of target values provided to the multiple-input/multiple output controller during each operational cycle of the process control system; and

an optimizer adapted to develop the set of target values for use by the multiple-input/multiple-output controller during each operational cycle of the process control system.

2. The process control system of claim 1, wherein the optimizer is a quadratic programming optimizer.

3. The process control system of claim 1, wherein the optimizer is a linear programming optimizer.

4. The process control system of claim 3, wherein the multiple-input/multiple-output controller is a model predictive controller.

5. The process control system of claim 1, wherein the multiple-input/multiple-output controller is a model predictive controller.

6. The process control system of claim 1, wherein the multiple-input/multiple-output controller is developed from a squared control matrix having the same number of inputs as outputs.

7. The process control system of claim 1, wherein the optimizer includes an objective function that defines costs or profits associated with at least one input to the process and associated with at least one output of the process.

8. The process control system of claim 1, wherein the optimizer produces the set of target values based predicted steady state values of control and auxiliary outputs of the process.

9. The process control system of claim 1, wherein the optimizer is a linear or quadratic programming optimizer including an objective function and the optimizer is adapted to minimize or maximize the objective function while keeping a set of control variables at predefined set points, a set of auxiliary variables within a set of predefined auxiliary variable constraint limits and a set of manipulated variables within a set of predefined manipulated variable constraint limits and, if no solution exists, to enable at least one control variables to be kept within a predefined set point range.

10. The process control system of claim 1, wherein the optimizer is a linear or quadratic programming optimizer including an objective function and the optimizer is adapted to minimize or maximize the objective function while keeping a set of control variables within predefined set point limits, a set of auxiliary variables within a set of predefined auxiliary variable limits and a set of manipulated variables within a set of predefined manipulated variable limits and, if no solution exists, to enable at least one of the auxiliary variable limits to be violated.

11. The process control system of claim 10, wherein the optimizer is adapted to store a set of priority numbers for the set of auxiliary variables and wherein the optimizer uses the priority numbers to determine the at least one of the auxiliary variable limits to be violated.

12. A process control system for controlling a process, comprising:

an optimizer adapted to determine an optimal operating point of the process based on a first number of predicted values of control and auxiliary variables of the process and based on a second number of current values of manipulated variables of the process; and

a multiple-input/multiple-output controller adapted to produce the second number of manipulated control signals to control the manipulated variables of the process based on a predetermined subset of the first number of predicted values of the control and auxiliary variables of the process, wherein the predetermined subset is different in number than the first number of control and auxiliary variables of the process.

13. The process control system of claim 12, wherein the predetermined subset is less than or is equal to the second number of manipulated variables.

14. The process control system of claim 12, wherein the multiple-input/multiple-output controller is a model predictive controller including a square control matrix.

15. The process control system of claim 14, wherein the square control matrix includes control and auxiliary variables of the process as inputs and manipulated variables of the process as outputs and includes a number of control and auxiliary variables that is equal to the number of manipulated variables.

16. The process control system of claim 14, wherein the square control matrix includes control and auxiliary variables of the process as inputs and manipulated variables of the process as outputs and includes a number of control and auxiliary variables less than the number of manipulated variables and has at least one of the manipulated variables as both a manipulated variable output and a control variable input.

17. The process control system of claim 16, wherein the number of the at least one of the manipulated variables assigned to be both manipulated variable outputs and control variable inputs is equal to the difference between the total number of manipulated variables and number of control and auxiliary variables used as control variable inputs to the control matrix.

18. The he process control system of claim 16, wherein the at least one of the manipulated variables used as both a manipulated variable output and a control variable input includes a step response with unity gain.

19. The process control system of claim 14, wherein the model predictive controller is generated to minimize squared control error over a prediction horizon and to minimize manipulated variables moves over a control horizon.

20. The process control system of claim 19, wherein the model predictive controller is generated to achieve the sum of manipulated variable moves over the control horizon equal to an optimal target change of the manipulated variables.

21. The process control system of claim 19, wherein a degree of satisfaction of minimizing squared error over the prediction horizon and minimizing manipulated variables moves can be adjusted arbitrarily.

22. The process control system of claim 19, wherein the degree of satisfaction of achieving optimal targets change of the manipulated variables can be adjusted arbitrarily.

23. The process control system of claim 12, wherein the optimizer is a linear or quadratic programming optimizer.

24. The process control system of claim 23, wherein the optimizer produces a set of target values for the predetermined subset of the first number of control and auxiliary variables and the multiple-input/multiple output controller combines the target values for the predetermined subset of the first number of control and auxiliary variables with measured values of the predetermined subset of the first number of control and auxiliary variables to produce the manipulated control signals.

25. The process control system of claim 24, wherein the multiple-input/multiple-output controller produces a set of predicted control and auxiliary variables and a set of predicted manipulated variables and wherein the optimizer uses the set of predicted control and auxiliary variables and the set of predicted manipulated variables to produce the set of target values for the predetermined subset of the first number of control and auxiliary variables.

26. The process control system of claim 25, further including a response matrix defining the reaction of each of the first number of control and auxiliary variables to a change in each of the second number of manipulated variables, wherein the optimizer produces a set of target manipulated variable values defining an optimal operating point and the optimizer uses the response matrix to determine the target values for the predetermined subset of the first number of control and auxiliary variable values from the set of target manipulated variable values.

27. The process control system of claim 26, wherein the optimizer is adapted to produce the set of target manipulated variable values that maximize or minimize an objective function while keeping each of the control variables at predefined set points and each of the auxiliary variables and manipulated variables within predefined constraint limits.

28. The process control system of claim 27, wherein the optimizer is adapted to produce the set of target manipulated variable values that maximize or minimize the objective function while keeping each of the control variables within predefined set point limits and each of the auxiliary variables and manipulated variables within constraint limits when a solution that keeps each of the control variables at predefined set points and each of the auxiliary variables and manipulated variables within predefined constraint limits does not exist.

29. The process control system of claim 28, wherein the optimizer is adapted to produce the set of target manipulated variable values that maximize or minimize the objective function while keeping each of the control variables within predefined set point limits and the manipulated variables within predefined constraint limits while allowing one or more of the auxiliary variables to violate predetermined constraint limits based on priorities associated with the auxiliary variables when a solution that keeps each of the control variables within predefined set point limits and each of the auxiliary variables and manipulated variables within predefined constraint limits does not exist.

30. A method of controlling a process having a plurality of manipulated variables and a multiplicity of control and auxiliary variables capable of being effected by changes in the manipulated variables, wherein the plurality of manipulated variables is different in number than the multiplicity of control and auxiliary variables, the method comprising;

selecting a subset of the multiplicity of control and auxiliary variables to use in performing process control;

creating a control matrix using the selected subset of the multiplicity of the control and auxiliary variables and the plurality of manipulated variables;

generating a controller from the control matrix having the selected subset of the multiplicity of the control and auxiliary variables as inputs and the plurality of manipulated variables as outputs;

performing process optimization by selecting a process operating point to minimize or maximize an objective function dependent on the plurality of manipulated variables and the multiplicity of control and auxiliary variables, said process operating point defined by a set of target values for the selected subset of the multiplicity control and auxiliary variables;

performing a multiple-input/multiple output control technique using a controller generated from the control matrix to develop a set of manipulated variable values from the target values for the selected subset of the multiplicity of control and auxiliary variables and measured values of the selected subset of the multiplicity of control and auxiliary variables; and

using the developed set of manipulated variable values to control the process.

31. The method of claim 30, wherein the steps of performing process optimization and performing the multiple-input/multiple-output control technique are performed during consecutive scan periods of the process.

32. The method of claim 30, wherein the steps of selecting the subset of the multiplicity of control and auxiliary variables and creating a control matrix are performed prior to on-line operation of the process.

33. The method of claim 30, wherein the step of selecting the subset of the multiplicity of control and auxiliary variables includes selecting each of the control or auxiliary variables within the subset of the multiplicity of control and auxiliary variables based on a minimum condition number for the control matrix.

34. The method of claim 30, wherein the step of selecting the subset of the multiplicity of control and auxiliary variables includes selecting one of the control or auxiliary variables as being most responsive to one of the manipulated variables based on a gain response of the one of the control or auxiliary variables to the one of the manipulated variables

35. The method of claim 30, wherein the step of selecting the subset of the multiplicity of control and auxiliary variables includes selecting one of the control or auxiliary variables as being most responsive to one of the manipulated variables based on a response time of the one of the control or auxiliary variables to the one of the manipulated variables.

36. The method of claim 30, wherein the step of selecting the subset of the multiplicity of control and auxiliary variables includes selecting one of the control or auxiliary variables as being most responsive to one of the manipulated variables based on a combination of a gain response and a response time of the one of the control or auxiliary variables to the one of the manipulated variables.

37. The method of claim 30, wherein the step of performing the multiple-input/multiple output process control technique includes the step of performing a model predictive control technique.

38. The method of claim 37, wherein the step of performing process optimization includes the step of performing a linear or quadratic programming technique.

39. The method of claim 30, wherein the step of selecting the subset of the multiplicity of control and auxiliary variables includes the step of selecting a single and different one of the control and auxiliary variables for each of the multiplicity of manipulated variables.

40. A process control element adapted to be used as a portion of a process control routine implemented on a processor to control a plurality of control and auxiliary parameters of a process using a multiplicity of manipulated parameters, the process control element comprising:

a computer readable medium;

a function block stored on the computer readable medium and adapted to be executed on the processor to implement multiple-input/multiple output control of the process during each control scan period, the function block including;

an objective function that defines an optimization criteria based on the plurality of control and auxiliary parameters;

an optimizer routine that uses the objective function to produce a set of optimal target values for the control and auxiliary parameters during each control scan period;

a control matrix that relates a predetermined subset of the plurality of control and auxiliary parameters to the multiplicity of manipulated parameters; and

a multiple-input/multiple-output control routine that produces a control signal for each of the multiplicity of manipulated parameters during each control scan period using the control matrix and the target values for the subset of the plurality of control and auxiliary variables, wherein the control signals are determined to drive the subset of the plurality of control and auxiliary parameters to the optimal target values for the subset of control and auxiliary parameters.

41. The process control element of claim 40, wherein the optimizer routine includes a linear or quadratic programming routine.

42. The process control element of claim 41, wherein the multiple-input/multiple-output control routine includes a model predictive control routine.

43. The process control element of claim 42, wherein the control matrix is squared and uses a first number of control and auxiliary parameters and the first number of manipulated parameters and wherein the objective function defines an optimization criteria based on a second number of control and auxiliary parameters, wherein the second number is different than the first number.

44. The process control element of claim 40, wherein the objective function is user selectable during process operation.

45. The process control element of claim 40, further including a plurality of different potential objective functions selectable as the objective function to be used by the optimizer routine.

46. The process control element of claim 40, wherein the function block includes a storage for storing a set of control parameter set points and a set of auxiliary and manipulated parameter limits and wherein the optimizer routine is configured to determine the set of optimal target values for the manipulated parameters which result in the control parameters being at the control parameter set points, the auxiliary and manipulated parameters being within the auxiliary and manipulated parameter limits and the objective function being minimized or maximized.

47. The process control element of claim 46, wherein the storage also stores a set of control parameter set point limits and the optimizer routine is adapted to produce the set of optimal target values for the manipulated parameters that maximize or minimize the objective function while keeping each of the control parameters within the control parameter set points limits and each of the auxiliary parameters and manipulated parameters within the auxiliary and manipulated parameter limits when a solution that keeps the control parameters at the control parameter set points and the auxiliary and manipulated parameters within the auxiliary and manipulated parameter limits does not exist.

48. The process control element of claim 47, wherein the storage also stores a set of priority indications for the auxiliary parameters and the optimizer routine is adapted to produce the set of target manipulated parameters that maximize or minimize the objective function while keeping each of the control parameters within the control parameter set points limits while allowing one or more of the auxiliary parameters to violate the auxiliary parameter limits based on the priority indications for the auxiliary parameters when a solution that keeps each of the control parameters within the control parameter set point limits and each of the auxiliary parameters and manipulated parameters within the auxiliary and manipulated parameter limits does not exist.

49. The process control element of claim 40, wherein the control routine is adapted to produce a predicted value for each of the control, auxiliary and manipulated parameters and to provide the predicted values for each of the control, auxiliary and manipulated parameters to the optimizer routine and wherein the optimizer routine is adapted to use the predicted values for the control, auxiliary and manipulated parameters to determine the target values for the subset of the control and auxiliary parameters.

50. A method of performing control of a process having a first number of control and auxiliary variables controlled by a second number of manipulated variables, the method comprising:

determining a step response matrix defining a response of each of the control and auxiliary variables to changes in each of the manipulated variables;

selecting a subset of the control and auxiliary variables, the subset having the same or less number of control and auxiliary variables as manipulated variables;

creating a square control matrix from the responses within the response matrix for the selected subset of the control and auxiliary variables and the manipulated variables; and

during each scan of the process;

obtaining a measure of each of the selected subset of the control and auxiliary variables;

calculating an optimal operating target value for each of the selected subset of the control and auxiliary variables;

performing a multiple-input/multiple-output control routine using the target values for the each of the selected subset of the control and auxiliary variables, the measures of the selected subset of the control and auxiliary variables and the control matrix to produce a set of manipulated parameter signals; and

using the manipulated parameter signals to control the process.

51. The method of performing control of a process of claim 50, wherein the steps of selecting the subset and creating a square control matrix are performed prior to on-line operation of the process.

52. The method of performing control of a process of claim 50, wherein the step of selecting the subset of the control and auxiliary variables includes selecting one of the control or auxiliary variables as being best responsive to one of the manipulated variables.

53. The method of performing control of a process of claim 50, wherein the step of selecting a subset of the control and auxiliary variables includes selecting one of the control or auxiliary variables as being best responsive to one of the manipulated variables based on a gain response of the one of the control or auxiliary variables to the one of the manipulated variables.

54. The method of performing control of a process of claim 50, wherein the step of selecting the subset of the control and auxiliary variables includes selecting one of the control or auxiliary variables as being best responsive to one of the manipulated variables based on a response time of the one of the control or auxiliary variables to the one of the manipulated variables.

55. The method of performing control of a process of claim 50, wherein the step of selecting the subset of the control and auxiliary variables includes selecting one of the control or auxiliary variables as being best responsive to one of the manipulated variables based on a combination of a gain response and a response time of the one of the control or auxiliary variables to the one of the manipulated variables.

56. The method of performing control of a process of claim 50, wherein the step of selecting the subset of the control and auxiliary variables includes automatically selecting the subset of the control and auxiliary variables based on the response matrix.

57. The method of performing control of a process of claim 50, wherein the step of selecting the subset of the control and auxiliary variables includes determining a condition number of a matrix including the selected subset of the control and auxiliary variables.

58. The method of performing control of a process of claim 50, wherein the step of creating the square control matrix includes using control and auxiliary variables of the process as inputs and manipulated variables of the process as outputs and selecting a number of control and auxiliary variables less than the number of manipulated variables and selecting at least one of the manipulated variables as a control variable input.

59. The method of performing control of a process of claim 50, wherein the step of performing the multiple-input/multiple output control routine includes the step of performing a model predictive control technique.

60. The method of performing control of a process of claim 59, wherein the step of calculating an optimal operating target value includes performing a linear or a quadratic programming technique.

61. The method of performing control of a process of claim 60, wherein the step of performing a linear programming technique includes using an objective function that defines an optimal operation of the process based on the first number of control and auxiliary variables and on the second number of manipulated variables.

62. The method of performing control of a process of claim 61, further including selecting one of a predetermined set of objective functions as the objective function to use within the linear programming technique.