Method and apparatus for providing multivariable nonlinear control
First Claim
1. A system for providing an apparatus for use in controlling a process at a desired setpoint level, the process having process inputs and outputs, said system comprising:
- (a) a data base of training patterns representing historical values of the process inputs and process outputs;
(b) a set of future time steps defining a future time horizon;
(c) a prediction model constructed with the training patterns contained in said data base, for predicting the process outputs over said future time horizon;
(d) a sensitivity processor utilizing said prediction model for determining the effect in the process outputs as a result of changes made to the historical values of the process inputs, said sensitivity processor producing predicted process outputs;
(e) a first processing element for computing a prediction time where a greatest value in the predicted process outputs occurs, the prediction time being an optimum prediction time;
(f) a second processing element for computing the predicted process output as advanced by the optimum prediction time;
(g) said apparatus comprising;
(i) an input means for receiving input variables for operating said apparatus, the input variables comprising the historical values of the process inputs and outputs from said data base, optimum prediction times, and the predicted process outputs as advanced by the optimum prediction times;
(ii) an output means for generating output variables for use in controlling the process in a preferred manner; and
(iii) a processing means for mapping said input means to said output means, said processing means comprising a function for performing said mapping, said function determining an optimum prediction time, and a predicted process output for performing said mapping, the optimum prediction time representing an effective response time of the process to a change in a desired setpoint level and the predicted process output representing a process output as advanced by the optimum prediction time;
(h) a training system for training said apparatus in accordance with a training algorithm; and
(i) a third processing element for configuring said apparatus to receive all of said input variables and for operating said training system to train said apparatus with all of said input variables thereby producing output variables for use in controlling the process.
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Abstract
A method and apparatus for training and optimizing a neural network for use in controlling multivariable nonlinear processes. The neural network can be used as a controller generating manipulated variables for directly controlling the process or as part of a controller structure generating predicted process outputs. The neural network is trained and optimized off-line with historical values of the process inputs, outputs, and their rates of change. The determination of the manipulated variables or the predicted process outputs are based on an optimum prediction time which represents the effective response time of the process output to the setpoint such that the greatest change to the process output occurs as a result of a small change made to its paired manipulated variable.
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Citations
37 Claims
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1. A system for providing an apparatus for use in controlling a process at a desired setpoint level, the process having process inputs and outputs, said system comprising:
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(a) a data base of training patterns representing historical values of the process inputs and process outputs; (b) a set of future time steps defining a future time horizon; (c) a prediction model constructed with the training patterns contained in said data base, for predicting the process outputs over said future time horizon; (d) a sensitivity processor utilizing said prediction model for determining the effect in the process outputs as a result of changes made to the historical values of the process inputs, said sensitivity processor producing predicted process outputs; (e) a first processing element for computing a prediction time where a greatest value in the predicted process outputs occurs, the prediction time being an optimum prediction time; (f) a second processing element for computing the predicted process output as advanced by the optimum prediction time; (g) said apparatus comprising; (i) an input means for receiving input variables for operating said apparatus, the input variables comprising the historical values of the process inputs and outputs from said data base, optimum prediction times, and the predicted process outputs as advanced by the optimum prediction times; (ii) an output means for generating output variables for use in controlling the process in a preferred manner; and (iii) a processing means for mapping said input means to said output means, said processing means comprising a function for performing said mapping, said function determining an optimum prediction time, and a predicted process output for performing said mapping, the optimum prediction time representing an effective response time of the process to a change in a desired setpoint level and the predicted process output representing a process output as advanced by the optimum prediction time; (h) a training system for training said apparatus in accordance with a training algorithm; and (i) a third processing element for configuring said apparatus to receive all of said input variables and for operating said training system to train said apparatus with all of said input variables thereby producing output variables for use in controlling the process. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for training an apparatus for controlling a process, the process having process inputs, process outputs comprising at least one controlled variable, and wherein the process is responsive to a manipulated variable used for changing the process as a function of the controlled variable, the method comprising the steps of:
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a) generating a model of the process to calculate predicted process outputs over a future time horizon, wherein the model is constructed using historical process data; b) applying the model to compute a change in the predicted process outputs that occurs as a result of a change made to the historical process data; c) determining a prediction time where an optimum change in the predicted process outputs occurs, the prediction time;
being an optimum prediction time;d) computing a predicted process output advanced by the optimum prediction time; and e) training the apparatus with the predicted process output as advanced by the optimum prediction time and the historical process data to generate a corresponding variable for use in controlling the process. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23)
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24. A method for generating a nonlinear function generator for controlling a process having process inputs, process outputs comprising at least one controlled variable, and wherein the process is responsive to at least one manipulated variable for changing the process as a function of the controlled variable, the method comprising the steps of:
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a) collecting a plurality of historical values of manipulated variables and historical values of the process outputs generated from operations of the process and collected over a time span; b) pairing the manipulated variables with the process outputs based on the historical values in accord with an optimization criteria; c) retrieving training data generated from operations of the process and collected over the time span; d) calculating a plurality of future time steps representing a future time horizon; e) collecting a set of controlled variables as advanced by the future time horizon; f) generating a prediction model using training input data, the paired manipulated variables and the process outputs based on the historical values, the plurality of future time steps, and the controlled variables as advanced by the future time horizon to compute predicted controlled variables as advanced by the future time horizon; g) applying the prediction model to determine changes in the predicted controlled variables that occur as a result of changes made to the historical values of the manipulated variables, said application producing change variables; h) selecting from the change variables, a subset representing the greatest changes in value; i) storing the future time steps of the change variables in the subset produced in said selection step, the future time steps denoted as the selection times; j) determining a greatest change from the range of changes defined by the subset of change variables produced from said selection step; k) extrapolating the future times at which the greatest change occurs, the future times being the optimum prediction times; l) computing predicted controlled variables as advanced by the optimum prediction times; and m) training the nonlinear function generator with the predicted controlled variables as advanced by the optimum prediction times and the historical process data to compute corresponding variables for use in controlling the process. - View Dependent Claims (25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37)
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26. A method as in claim 39 wherein said nonlinear function generator generates said optimum prediction times.
Specification