Model predictive control apparatus and method

• US 5,519,605 A
• Filed: 10/24/1994
• Issued: 05/21/1996
• Est. Priority Date: 10/24/1994
• Status: Expired due to Fees

• Current Assignee: OLIN Corporation
• Original Assignee: OLIN Corporation
• Inventors: Cawlfield, David W.
• Primary Examiner(s): RUGGIERO, JOSEPH F
• Application Number:
  US08/328,100
• Time in Patent Office: 575 Days
• Field of Search: 364/164, 364/165, 364/148, 364/149-151, 364/152, 364/157, 364/158, 364/176, 364/178, 318/561
• US Class Current: 700/31
• CPC Class Codes: G05B 13/048 using a predictor

Abstract

A model predictive control apparatus and method for controlling the operation of a process having a process input signal and a process output signal, which includes determining a predicted process output signal at a future steady state condition according to a process model, determining a steady state error signal according to the difference between the predicted process output signal and a desired set point signal, and determining a set of future process input change signals required to correct for the estimated steady state error by providing at least one step response in the process output at a future time. The next net process input change is then applied according to the sum of the currently determined first element of the set of future process input change signals summed with any future process input change signals that were previously calculated for the next process input change signal that is calculated according to this method. The model is expressed in velocity form. Process tuning parameters are available to dampen the net process input change as well as the currently determined first element of the process input change with respect to a process input change signal that is calculated according to the steady state error divided by the steady state process gain.

Claims

1. An electronic model predictive control apparatus for controlling a plant having a plant input signal and a plant output signal dependent on said plant input signal, comprising:

a first memory containing a vector of data elements derived from a pseudo-inverse of an impulse response matrix model of said plant;

a second memory containing data indicative of future plant input signal changes calculated during prior control cycles;

a third memory containing a vector of past plant input signal changes;

a fourth memory containing data elements by way of which a future plant output signal change due to past plant input signal changes may be derived from said vector of past plant input signal changes;

an electronic processor adapted to;

receive a plant output signal sample;

calculate an expected future plant output signal from said data indicative of future plant input signal changes calculated during prior control cycles, said plant'"'"'s steady-state gain, said future plant output signal change due to past plant input signal changes, and said plant output signal sample;

calculate an error signal from a setpoint signal and said expected future plant output signal; and

calculate a plant input signal change from said error signal, said vector of data elements derived from said pseudo-inverse, and said data indicative of future plant input signal changes calculated during prior control cycles;

where said plant input signal change is applied to said plant input signal for controlling said plant output signal.

2. The control apparatus of claim 1, wherein said first, second, third and fourth electronic memories are embodied in a single memory device.

3. The control apparatus of claim 2, wherein said memory device is a RAM.

4. The control apparatus of claim 1, wherein said processor is under program control.

5. The control apparatus of claim 1, wherein said processor is implemented by direct electrical connection.

6. The control apparatus of claim 1, wherein said plant input signal change is further processed prior to being applied to said plant input signal in order to clip said plant input signal change to restrict said plant input signal to a threshold level, where said further processing includes:

determining when said plant input signal added to said plant input signal change exceeds said threshold level and computing a clip value equal to an amount by which said threshold level is exceeded;

subtracting said clip value from said plant input signal change;

adding twice said clip value to a plant input signal change calculated for the next control cycle;

subtracting said clip value from a plant input signal change calculated for two control cycles in the future.

7. The control apparatus of claim 1, wherein said plant input signal change is further processed prior to being applied to said plant input signal in order to clip said plant input signal change to restrict said plant input signal to a threshold level, where said further processing includes:

determining when said plant input signal added to said plant input signal change exceeds said threshold level; and

modifying the calculated plant input signals over a plurality of control cycles so that the sum of said modifications equals zero.

8. The control apparatus of claim 1, wherein said error signal is multiplied by a dampening factor.

9. The control apparatus of claim 1, wherein a partitioning factor is used to apportion the contribution to said plant input signal change due to said vector of data elements derived from said pseudo-inverse and at least one other vector derived from said pseudo-inverse.

10. The control apparatus of claim 9, wherein said at least one other vector derived from said pseudo-inverse is a single element vector comprising the inverse of said plant'"'"'s steady-state gain.

11. The control apparatus of claim 1, wherein said vector of data elements derived from said pseudo-inverse is a vector comprising the elements of an optimal column of said pseudo-inverse.

12. The control apparatus of claim 11, wherein said optimal column of said pseudo-inverse is at least partially determined by multiplying said impulse response matrix model by said pseudo-inverse, subtracting an identity matrix, and computing the least squares error.

13. The control apparatus of claim 12, wherein said optimal column of said pseudo-inverse is further determined by selecting the best left-most column in order to provide an early plant output signal response.

14. The control apparatus of claim 1, wherein said vector of data elements derived from said pseudo-inverse is a vector comprising a weighted average of at least two columns of said pseudo-inverse.

15. The control apparatus of claim 1, wherein said vector of data elements derived from said pseudo-inverse is a vector comprising an interpolation between two columns of said pseudo-inverse.

16. The control apparatus of claim 1, wherein said second and third memories comprise shift registers.

17. The control apparatus of claim 1, wherein said data elements contained in said fourth memory comprise a vector where each element corresponds to the sum of an A^o column, where A^o is the integral form of said impulse response matrix.

18. The control apparatus of claim 1, wherein said data elements contained in said fourth memory comprise a matrix where elements correspond to elements in a A^o matrix, where A^o is the integral form of said impulse response matrix.

19. The control apparatus of claim 1, wherein said plant comprises a fluid flow system having a flow measuring transducer and a flow control valve, where said plant output signal is derived from said flow measuring transducer, and said plant input signal is applied to said flow control valve.

20. The control apparatus of claim 1, wherein said plant comprises a chemical reactor system having a quality measure transducer and an actuator having an effect on said quality measure, where said plant output signal is derived from said quality measure transducer, and said plant input signal is applied to said actuator.

21. The control apparatus of claim 1, wherein said plant comprises a chemical reactor system having a flow controller, wherein said plant input signal is applied to a setpoint of said flow controller.

22. The control apparatus of claim 1, wherein said plant comprises a storage device having a read and/or write head, a head actuator, and rotating storage media, where said plant output signal is related to the position of said head over said storage media, and said plant input signal is applied to said head actuator to control the position of said head over said storage media.

23. The control apparatus of claim 1, wherein said vector of data elements derived from said pseudo-inverse has three data elements.

24. The control apparatus of claim 1, wherein said vector of data elements derived from said pseudo-inverse has a number of data elements equal to the order of the dynamics of a model of said plant plus one.

25. The control apparatus of claim 1, wherein said pseudo-inverse is calculated by singular value decomposition.

26. The control apparatus of claim 1, wherein said impulse response matrix is derived from an impulse response vector having about twenty elements.

27. The control apparatus of claim 1, wherein new modeling information is obtained during a setpoint change, and said new modeling information is used for obtaining a new model, thereby implementing adaptive control.

28. The control apparatus of claim 1, further comprising a CRT and keyboard for providing said setpoint signal to said processor.

29. The control apparatus of claim 1, wherein said impulse response matrix is derived from an impulse response vector determined by linear regression.

30. A model predictive control method for controlling a process having a process input signal and a process output signal dependent on said process input signal, comprising the steps of:

sampling said process output signal;

calculating an expected future process output signal from data indicative of future process input signal changes calculated during prior control cycles, said process'"'"' steady-state gain, a future process output signal change due to past process input signal changes, and said process output signal sample;

calculating an error signal from a setpoint signal and said expected future process output signal;

calculating a process input signal change from said error signal, a vector of data elements derived from a pseudo-inverse of an impulse response matrix model of said process, and said data indicative of future process input signal changes calculated during prior control cycles; and

applying said process input signal change to said process input signal for controlling said process output signal.

31. The control method of claim 30, wherein said process input signal change is further processed prior to being applied to said process input signal in order to clip said process input signal change to restrict said process input signal to a threshold level;

where said further processing includes the steps of;

determining when said process input signal added to said process input signal change exceeds said threshold level and computing a clip value equal to an amount by which said threshold level is exceeded;

subtracting said clip value from said process input signal change;

adding twice said clip value to a process input signal change calculated for the next control cycle;

subtracting said clip value from a process input signal change calculated for two control cycles in the future.

32. The control method of claim 30, wherein said plant input signal change is further processed prior to being applied to said plant input signal in order to clip said plant input signal change to restrict said plant input signal to a threshold level, where said further processing includes:

determining when said plant input signal added to said plant input signal change exceeds said threshold level; and

modifying the calculated plant input signals over a plurality of control cycles so that the sum of said modifications equals zero.

33. The control method of claim 30, further comprising the step of multiplying said error signal by a dampening factor.

34. The control method of claim 30, further comprising the step of apportioning, by way of a partitioning factor, the contribution to said process input signal change due to said vector of data elements derived from said pseudo-inverse and at least one other vector derived from said pseudo-inverse.

35. The control method of claim 34, wherein said at least one other vector derived from said pseudo-inverse is a single element vector comprising the inverse of said process'"'"' steady-state gain.

36. The control method of claim 30, wherein said vector of data elements derived from said pseudo-inverse is a vector comprising the elements of an optimal column of said pseudo-inverse.

37. The control method of claim 36, wherein said optimal column of said pseudo-inverse is at least partially determined by multiplying said impulse response matrix model by said pseudo-inverse, subtracting an identity matrix, and computing the least squares error.

38. The control method of claim 37, wherein said optimal column of said pseudo-inverse is further determined by selecting the left-most column having an acceptable least squares error in order to provide an early process output signal response.

39. The control method of claim 30, wherein said vector of data elements derived from said pseudo-inverse is a vector comprising a weighted average of at least two columns of said pseudo-inverse.

40. The control method of claim 30, wherein said vector of data elements derived from said pseudo-inverse is a vector comprising an interpolation between two columns of said pseudo-inverse.

41. The control method of claim 30, further comprising the step of deriving said future process output signal change due to past process input signal changes by multiplying a vector of past process input signal changes by a vector where each element corresponds to the sum of an A^o column, where A^o is the integral form of said impulse response matrix.

42. The control method of claim 30, further comprising the step of deriving said future process output signal change due to past process input signal changes by multiplying a vector of past process input signal changes by a matrix where elements correspond to elements in a A^o matrix, where A^o is the integral form of said impulse response matrix;

and summing the resulting vector.

43. The control method of claim 30, wherein said process comprises a fluid flow system having a flow measuring transducer and a flow control valve, further comprising the steps of deriving said process output signal from said flow measuring transducer, and applying said process input signal to said flow control valve.

44. The control method of claim 30, wherein said process comprises a chemical reactor system having a quality measure transducer and an actuator having an effect on said quality measure, further comprising the steps of deriving said process output signal from said quality measure transducer, and applying said process input signal to said actuator.

45. The control method of claim 30, wherein said process comprises a chemical reactor system having a flow controller, wherein said process input signal is applied to a setpoint of said flow controller.

46. The control method of claim 30, wherein said process comprises a data storage access system having a read and/or write head, a head actuator, and rotating storage media, further comprising the steps of deriving said process output signal from the position of said head over said storage media, and applying said process input signal to said head actuator to control said head.

47. The control method of claim 30, further comprising the steps of obtaining new modeling information during a setpoint change, and using said new modeling information for generating a new model, thereby implementing adaptive control.

48. The control method of claim 30, further comprising the step of determining an impulse response vector by linear regression;

and deriving said impulse response matrix from said impulse response vector.

49. The control method of claim 30, further comprising the steps of:

determining, according to said impulse response matrix model, optimum tuning constants for PI and/or PID controllers, and storing said constants for selective control of said process by alternate control techniques.

50. A method for controlling the operation of a process having a process input signal and a process output signal dependent upon said process input signal, said method comprising the steps of:

acquiring a sample of said process output signal;

determining a predicted process output signal at a future steady state condition according to a process model and said sample of said process output signal;

determining a steady state error signal according to the difference between said predicted process output signal and a desired setpoint signal;

determining a set of future process input signal changes calculated so that if applied to said process input signal said estimated steady state error would be substantially cancelled by causing at least one approximate step response in said process output signal at a future time; and

applying a first member of said set of future process input signal changes to said process input signal for controlling the operation of said process.

51. The method according to claim 50, wherein said step of determining a set of future process input signal changes includes the step of:

scaling a predetermined vector of data elements by said steady state error signal, said data elements representing an optimal column of a pseudo-inverse of a matrix of said process model.

52. The method of claim 51, wherein said set of future process input signal changes, if applied to said process input signal, would cause a single approximate step response in said process output signal at a future time.

53. The method according to claim 50, wherein said step of determining a set of future process input signal changes includes the step of:

scaling a predetermined vector of data elements by said steady state error signal, said data elements representing a weighted average of at least two columns of a pseudo-inverse of a matrix of said process model.

54. The method of claim 53, wherein said set of future process input signal changes, if applied to said process input signal, would cause a single approximate step response in said process output signal at a future time.

55. The method of claim 50, wherein said step of determining a set of future process input signal changes includes the step of:

scaling a predetermined vector of data elements by said steady state error signal, said data elements representing an interpolation between two columns of a pseudo-inverse of a matrix of said process model.

56. The method of claim 55, wherein said set of future process input signal changes, if applied to said process input signal, would cause a single approximate step response in said process output signal at a future time.

57. The method of claim 50, wherein said process is a fluid flow process, where said process input signal controls flow rate and where said process output signal provides an indication of flow rate.

58. The method of claim 50, wherein said process is a chemical reactor process, where said process input signal effects a quality measure, and where said process output signal provides an indication of said quality measure.

59. The method of claim 50, wherein said process is a data access process for a data storage device, where said process output signal is related to the movement of a data head, and where said plant input signal controls the movement of said data head.

60. An electronic model predictive control apparatus for controlling a process having a process input signal and a process output signal dependent on said process input signal, comprising:

electronic memory means for storing a vector of data elements derived from one or more columns of a pseudo-inverse of an impulse response matrix model of said process;

means for receiving a process output signal sample;

electronic processor means for calculating an expected future process output signal, for calculating an error signal from a setpoint signal and said expected future process output signal, and for calculating a process input signal change from said error signal, said vector of data elements derived from one or more columns of said pseudo-inverse, and data indicative of future process input signal changes calculated during prior control cycles; and

means for applying said process input signal change to said process input signal for controlling said process output signal.

61. The control apparatus of claim 60, wherein said vector of data elements represents an optimal column of said pseudo-inverse of said impulse response matrix model of said process.

62. The control apparatus of claim 61, wherein said optimal column is selected by determining the column which when used as a set of input signal changes to said process would produce the best approximate step response output according to a predetermined criteria.

63. The control apparatus of claim 62, wherein said criteria includes least squares error.

64. The control apparatus of claim 60, further comprising data entry means for providing said setpoint signal to said processor means.

65. A method for controlling the operation of a process having a plurality of process input signals and one process output signal dependent upon at least one of said process input signals, said method comprising the steps of:

acquiring a sample of said process output signal;

determining a predicted process output signal at a future steady state condition according to a process model and said sample of said output signal;

determining a steady state error signal according to the difference between said predicted process output signal and a desired setpoint signal;

determining, for each process input signal, a set of future process input signal changes calculated so that if applied to said plurality of process input signals said estimated steady state error would be substantially cancelled by causing at least one approximate step response in said process output signal at a future time; and

applying a first member from each said set of future process input signal changes to a corresponding one of said process input signals for controlling the operation of said process.

66. The method of claim 65, wherein said set of future process input signal changes for each process input signal, if applied to said plurality of process input signals, would cause a single approximate step response.

67. A method for controlling the operation of a process having a plurality of process input signals and a plurality of process output signals each process output signal dependent upon at least one of said process input signals, said method comprising the steps of:

acquiring a corresponding sample of each of said plurality of process output signals;

determining, for each of said plurality of process output signals, a corresponding predicted process output signal at a future steady state condition according to a process model and said corresponding sample;

determining, for each of said plurality of process output signals, a corresponding steady state error signal according to the difference between said corresponding predicted process output signal and a corresponding setpoint signal;

determining, for each process output signal, a plurality of sets of future process input signal changes, each set corresponding to one of said plurality of process input signals, calculated so that if applied to said plurality of process input signals said corresponding estimated steady state error would be substantially cancelled by causing at least one approximate step response in a corresponding one of said plurality of process output signals at a future time; and

applying, to each one of said plurality of process input signals, a sum of first members from corresponding sets of future process input signal changes for controlling the operation of said process.

68. The method of claim 67, wherein said plurality of sets of future process input signal changes, if applied to said plurality of process input signals, would cause a single approximate step response.

69. A model predictive control method for controlling a process having a process input signal and a process output signal dependent on said process input signal, comprising the steps of:

sampling said process output signal;

calculating an expected future process output signal from a future process output signal change due to past process input signal changes and said process output signal sample;

calculating an error signal from a setpoint signal and said expected future process output signal;

calculating a process input signal change from said error signal multiplied by the inverse of said process'"'"' steady-state gain, said steady-state process gain determined according to an impulse response model of said process; and

applying said process input signal change to said process input signal for controlling said process output signal.