Data processing: generic control systems or specific application – Generic control system – apparatus or process – Optimization or adaptive control
Reexamination Certificate
2000-06-27
2003-12-09
Gordon, Paul P. (Department: 2121)
Data processing: generic control systems or specific application
Generic control system, apparatus or process
Optimization or adaptive control
C700S031000, C700S034000, C700S049000, C700S050000, C700S074000
Reexamination Certificate
active
06662058
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention is directed to an adaptive predictive expert control system for controlling single-input single-output, or multivariable time-variant processes, with known or unknown parameters and with or without time delay. More particularly, the present invention is directed to a system with an expert block controlling previously known adaptive-predictive control systems. This expert block operates using rules that can determine and/or modify the operation of a driver block, a control block and an adaptive mechanism. The expert block can accommodate evolution of the process input/output (I/O) variables used with the above blocks and mechanisms. Application of the expert block to control systems defined in previous adaptive-predictive methodologies improves performance, robustness and stability of the overall system.
2. Description of the Related Prior Art
The application of adaptive-predictive control systems using adaptive-predictive controllers is well known. Such adaptive-predictive control systems are described in European Patent No. 0037579, issued Aug. 20, 1986, entitled “Adaptive-Predictive Control Method and Adaptive-Predictive Control System”, U.S. Pat. No. 4,358,822 issued Nov. 9, 1982, entitled “Adaptive-Predictive Control System”, which is hereby incorporated by reference; U.S. Pat. No. 4,197,576, issued Apr. 8, 1980, entitled “Adaptive-Predictive Control System”; and United Kingdom Patent No. 1583545, dated Jul. 1, 1977 and entitled “Improvements In an Relating to Control Systems”, all issued to the present applicant.
The adaptive-predictive controllers are operable to predict, by means of an adaptive-predictive (AP) model included in the control block, the value of a set of dynamic process output variables. The set of dynamic process output variables form a dynamic process output vector at a future sampling instant. The adaptive-predictive controller also generate at each sampling instant, using the AP model, a predicted control vector that causes the predicted dynamic process output vector to equal a desired dynamic process output vector at the future sampling instant. The desired dynamic process output vector is generated by a driver block according to desired performance criterion.
In addition, adaptive-predictive controllers include an adaptive mechanism that periodically updates the parameters of the AP model within the control block. The updates occur in such a way that the difference between the actual value of the dynamic process output vector at the future sampling instant and the value of the dynamic process output vector predicted by the control block is reduced towards zero.
Adaptive-predictive control systems have proven reliability and excellent performance when applied to industrial processes. However, their performance, robustness and stability become less reliable when the controlled process is very non-linear, is time varying and/or evolves in the presence of strong noises and perturbations. In these situations, it must be determined when the adaptive predictive control can be applied successfully, and when it may be advantageous to use the available real time process information to model the input/output relationship of the process. Thus, a new control solution is desired wherein:
a) The experience in the application of adaptive-predictive control could be used (i) to develop rules to determine in real time when adaptive-predictive control is advisable; and (ii) when adaptive-predictive control is advisable, to develop additional rules to determine how it must be applied and when adaptation of the AP model parameters must be performed.
b) When adaptive-predictive control is not advisable, the experimental knowledge of the human operator should be taken in to account by a further set of rules that will apply an “intelligent” control vector to the process.
The present invention is an improvement over the previously disclosed adaptive-predictive control systems, as indicated in the above mentioned U.S. Pat. Nos. 4,358,822 and 4,197,576, and United Kingdom Patent No. 1583545.
SUMMARY OF THE INVENTION
The adaptive-predictive expert control system of the present invention adds an expert block into the operation of previously known adaptive-predictive control systems. The expert control determines and/or modifies the operation of the driver block, the control block and the adaptive mechanism of the previous art. The expert block operates with different sets of rules, for example:
a) A first set of rules which can determine whether or not the control block can use the AP model to generate a control vector by applying adaptive-predictive control as defined by the previous art.
b) When the AP model can be used to generate the control vector, a second set of rules can determine whether or not the parameters of the AP model can be updated from the real time process I/O variables measurements.
c) When the AP model can be used to generate the control vector, a third set of rules can determine whether or not control limits applied to the predicted control vector must be reduced appropriately.
d) When the AP model should not be used to generate the control vector, the control block will use a fourth set of rules, based on the human operator control experience, to generate the control vector to be applied to the process.
e) When the AP model can be used to generate the control vector, a fifth set of rules can determine whether or not the performance criterion of the driver block must be redefined.
f) When the AP model can be used to generate the control vector, a sixth set of rules can determine whether or not the parameters of the AP model must be reinitialized to some predefined values.
The above considered sets of rules, within the expert block, imitate in different ways human intelligence. For instance, these rules can take into account specific domains in which the process I/O variables may reside and the length of “time of residence”, understanding by “time of residence” the number of consecutive control periods that the process I/O variables remain in a specific domain.
The relation between the sets of rules and the specific domains and times of residence may be defined, for instance, as follows:
1) The first set of rules may examine a first domain and a first time of residence for a dynamic process output vector, containing at least one process output variable. The first set of rules can then determine that adaptive-predictive control will be applied when the dynamic process output vector resides in the first domain for a period of time in excess of the predetermined first time of residence.
2) The second set of rules may examine a second domain and a second time of residence for the dynamic process output vector. The second set of rules can determine that adaptation by updating the AP model parameters should be stopped while adaptive-predictive control is applied. The updates to the AP model can be halted when the dynamic process output vector resides in the second domain for a period of time in excess of the second time of residence.
3) The third set of rules may examine a third domain and a third time of residence for the dynamic process output vector. The third set of rules can carry out an appropriate tightening of control limits while adaptive-predictive control is applied. The tightening of control limits is applied when the dynamic process output vector resides in the third domain for a period of time in excess of the third time of residence.
In addition, while the dynamic process output vector is in the domain for adaptive-predictive control, the expert block will always be able to modify the control block parameters and/or to redefine the driver block performance criterion taking into account the particular operating conditions of the process and the desired performance of the control system. Thus:
4) The fifth set of rules may examine the dynamic process output vector evolution to redefine the driver block performance criterion according to a desired control system performance. A
Gordon Paul P.
Ostrolenk, Faber, Gerb & Soffen, LL
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