Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
1998-08-28
2003-04-29
Starks, Jr., Wilbert L. (Department: 2121)
Data processing: artificial intelligence
Neural network
Learning task
C706S014000, C706S016000, C706S025000
Reexamination Certificate
active
06556980
ABSTRACT:
FIELD OF THE INVENTION
The invention relates to industrial process control, and more particularly to an improved method and apparatus for model-free adaptive control of industrial processes using enhanced model-free adaptive control architecture and algorithms as well as feedforward compensation for disturbances.
BACKGROUND OF THE INVENTION
A Model-Free Adaptive Control methodology has been described in patent application Ser. No. 08/944,450 filed on Oct. 6, 1997. The methodology of that application, though effective and useful in practice, has some drawbacks as follows:
1. The model-free adaptive controller includes a nonlinear neural network which may cause saturation when the controller output is close to its upper or lower limits;
2. It is difficult for the user to specify a proper sample interval because it is related to the controller behavior;
3. Changing the controller gain in the absence of error may still cause a sudden change in controller output;
4. The prior multivariable model-free adaptive controller is quite complex and requires the presence of all sub-processes in the multi-input-multi-output process;
5. The static gain of the predictor in the prior anti-delay MFA controller is set at 1. It is better if the setting is related to the controller gain.
6. The time constant of the predictor in the prior anti-delay MFA controller is related to the setting of the sample interval. It is better if the setting is related to the process time constant;
SUMMARY OF INVENTION
The present invention overcomes the above-identified drawbacks of the prior art by providing model-free adaptive controllers using a linear dynamic neural network. The inventive controller also uses a scaling function to include the controller gain and user estimated process time constant. The controller gain can compensate for the process steady-state gain, and the time constant provides information of the dynamic behavior of the process. The setting for the sample interval becomes selectable through a wide range without affecting the controller behavior. Two more multivariable model-free adaptive controller designs (compensation method and prediction method) are disclosed. An enhanced anti-delay model-free adaptive controller is introduced to control processes with large time delays. The method to select the parameters for the anti-delay MFA predictor is disclosed. A feedforward/feedback model-free adaptive control system with two designs (compensation and prediction method) is used to compensate for measurable disturbances.
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Starks, Jr. Wilbert L.
Stout, Uxa Buyan & Mullins, LLP
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