Adaptive-predictive control system

• US 4,197,576 A
• Filed: 08/04/1977
• Issued: 04/08/1980
• Est. Priority Date: 08/04/1976
• Status: Expired due to Term

• Current Assignee: Juan Martin Sanchez
• Original Assignee: Juan Martin Sanchez
• Inventors: Martin Sanchez, Juan
• Primary Examiner(s): Ruggiero, Joseph F.
• Application Number:
  US05/821,600
• Time in Patent Office: 978 Days
• Field of Search: 364/105, 364/106, 364/108, 364/118, 364/121, 364/501, 364/116
• US Class Current: 700/31
• CPC Class Codes: B01D 3/425 Head-, bottom- and feed stream
• CPC Class Codes: G05B 13/048 using a predictor

Abstract

An adaptive-predictive control system for controlling single-input single-output, or multivariable time-variant processes with known or unknown parameters and with or without time delay, is disclosed. The adaptive-predictive control system of the present invention updates on real time the parameters of an adaptive-predictive model from which the dynamic output vector of the process being controlled is predicted and the control vector controlling the operation of this process is computed with the objective that the predicted dynamic output vector becomes equal to the desired dynamic output vector.

Claims

1. A method for generating a control vector during each of a plurality of sampling instants k, said control vector to be applied to an apparatus which carries out a process having at least one input variable and at least one output variable, at least one of said input variables defining a process input vector, said apparatus varying said process input vector in accordance with the value of said control vector, said method comprising the steps of:

(A) storing a model which is capable of predicting the dynamic value of a process output vector, which vector is composed of at least one of said process output variables, at a future sampling instant k+r+1 as a function of said control vector;

(B) generating a desired dynamic process output vector at each of said sampling instants k, said desired dynamic process output vector being representative of a desired value of said process output vector at said future instant k+r+1; and

(C) generating, at each of said sampling instants k, that control vector which said model predicts will cause said dynamic process output vector to be equal to said desired dynamic process output vector at said future sampling instant k+r+1.

2. The method of claim 1, wherein said desired dynamic process output vector is generated taking into account the desired dynamics for said process and as a function of both a desired steady state process output vector and said dynamic process output vector.

3. The method of claim 2, wherein said step of generating said desired dynamic process output vector includes the step of generating an incremental desired dynamic output vector representative of the incremental difference between the desired dynamic process output vector and said dynamic process output vector.

4. The method of claim 1 wherein said control vector is an incremental control vector representative of the incremental variation in the input vector of said process which said model predicts will cause said dynamic process output vector to be equal to said desired dynamic output vector at said future sampling instant k+r+1.

5. The method of claim 3 wherein said control vector is an incremental control vector representative of the incremental variation in the input vector of said process which said model predicts will cause said dynamic process output vector to be equal to said desired dynamic output vector at said future sampling instant k+r+1.

6. The method of claim 1, further including the step of periodically updating the parameters of said model in such a manner that the difference between the actual dynamic process output vector at sampling instant k+r+1 and the dynamic process output vector which said model predicted would result at sampling instant k+r+1 is reduced towards zero.

7. The method of claim 6, wherein said step of updating the parameters of said model comprises the steps of:

(A) periodically generating an estimated process output vector representative of the dynamic process output vector which said model, as updated during some first predetermined prior sampling instant, estimates should have occurred at sampling instant k as a result of the generation of said control vector at said prior sampling instant k-r-1;

(B) periodically generating an estimated error vector representative of the difference between said estimated process output vector at said sampling instant k and said dynamic process output vector at said sampling instant k;

(C) periodically modifying the parameters of said model as a function of said estimated error vector.

8. The method of claim 7, further including the step of generating an incremental process output vector representative of the difference between the actual dynamic process output vector at instant k and the actual dynamic process output vector at some second prior sampling instant.

9. The method of claim 8, wherein said estimated process output vector is the value estimated by the model, as updated at said first prior sampling instant, of the incremental dynamic process output vector.

10. The method of claim 9, wherein said step of generating an estimated error vector comprises the step of determining the difference between said incremental process output vector and said estimated process output vector.

12. The method of claim 1 further including the step of applying said control vector to said apparatus in a manner which will cause said apparatus to vary said process input vector in accordance therewith.

11. A control system for controlling a process having at least one input variable and at least one output variable, said control system comprising:

driver block means responsive to a set point vector v(k) and an instantaneous process output vector y_p (k) for generating a desired incremental process output vector d₁ (k+r+1) during each of a plurality of sampling intervals k, said desired incremental output vector d₁ (k+r+1) corresponding to the desired incremental change in output vector of said process between the sampling interval k and the sampling interval k+r+1;

control block means responsive to said desired incremental output vector d₁ (k+r+1) for generating an incremental control vector u(k) during each said sampling interval k in accordance with an adaptive-predictive model, said adaptive-predictive model serving to predict the process output vector and to determine the incremental control vector u(k) which must be applied to the process during sampling interval k to make the predicted process output vector equal to the desired process output vector during sampling interval k+r+1 as determined by said incremental desired output vector d₁ (k+r+1);

identification block means responsive to said incremental control vector u(k) and an incremental process output vector y(k) for generating an estimated incremental process output vector d(k) during each said sampling interval k in accordance with said adaptive-predictive model said estimated incremental process output vector d(k) being representative of the incremental process output which the adaptive-predictive model, as updated during a sampling interval prior to sampling interval k, predicts should occur during interval k as a result of the generation of the incremental control vector u(k-r-1) during sampling interval k-r-1;

means for generating an estimated error vector e(k) during each of said sampling intervals k, said estimated error vector e(k) being representative of the difference between said estimated incremental process output vector d(k) and said incremental output vector y(k); and

feedback means responsive to said estimated error vector e(k) for modifying the parameters of said adaptive predictive model during each said sampling interval k, said feedback means to modify the parameters of said adaptive-predictive model in such a manner that said estimated error vector e(k) is reduced towards zero.