Data processing: generic control systems or specific application – Generic control system – apparatus or process – Optimization or adaptive control
Reexamination Certificate
1998-12-31
2003-04-01
Voeltz, Emanuel Todd (Department: 2121)
Data processing: generic control systems or specific application
Generic control system, apparatus or process
Optimization or adaptive control
C703S002000
Reexamination Certificate
active
06542782
ABSTRACT:
COPYRIGHT NOTICE
A portion of the disclosure of this patent document (software listings in Appendices A and B) contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of this patent document or the patent disclosure, as it appears in the United States Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights and protection whatsoever.
COMPUTER PROGRAM LISTING APPENDIX
A computer program listing appendix that lists the steps of a computer program that is used in carrying out the present invention is set forth in Appendix A and in Appendix B. The computer program listing in Appendix A and in Appendix B is provided on a Compact Disc—Read Only Memory(CD-ROM) in accordance with 37 CFR §1.52(e). The computer program listing appendix that is set forth in Appendix A and in Appendix B is hereby incorporated by reference in this document for all purposes. A copy of Appendix A and a copy of Appendix B are on CD-ROM Copy 1 and duplicate copies of Appendix A and Appendix B are on CD-ROM Copy 2. Each CD-ROM contains a file entitled “AppendixCD-ROM” that is 51 KB in length and that was created on Jun. 17, 2002.
TECHNICAL FIELD OF THE INVENTION
The present invention is directed, in general, to control systems for process facilities and, more specifically, to systems for generating and using lookup tables with process facility control systems and models of the same, and methods of operating such systems, all for use to optimize process facilities.
BACKGROUND OF THE INVENTION
Presently, process facilities (e.g., a manufacturing plant, a mineral or crude oil refinery, etc.) are managed using distributed control systems. Contemporary control systems include numerous modules tailored to control or monitor various associated processes of the facility. Conventional means link these modules together to produce the distributed nature of the control system. This affords increased performance and a capability to expand or reduce the control system to satisfy changing facility needs.
Process facility management providers, such as Honeywell, Inc., develop control systems that can be tailored to satisfy wide ranges of process requirements (e.g., global, local or otherwise) and facility types (e.g., manufacturing, refining, etc.). A primary objective of such providers is to centralize control of as many processes as possible to improve an overall efficiency of the facility. Each process, or group of associated processes, has certain input (e.g., flow, feed, power, etc.) and output (e.g., temperature, pressure, etc.) characteristics associated with it.
In recent years, model predictive control (“MPC”) techniques have been used to optimize certain processes as a function of such characteristics. One technique uses algorithmic representations to estimate characteristic values (represented as parameters, variables, etc.) associated with them that can be used to better control such processes. In recent years, physical, economic and other factors have been incorporated into control systems for these associated processes. Examples of such techniques are described in U.S. Pat. No. 5,351,184 entitled “Method of Multivariable Predictive Control Utilizing Range Control;” U.S. Pat. No. 5,561,599 entitled “Method of Incorporating Independent Feedforward Control in a Multivariable Predictive Controller;” U.S. Pat. No. 5,574,638 entitled “Method of Optimal Scaling of Variables in a Multivariable Predictive Controller Utilizing Range Control;” U.S. Pat. No. 5,572,420 entitled “Method of Optimal Controller Design of Multivariable Predictive Control Utilizing Range Control” (the “'420 Patent”); U.S. patent application Ser. No. 08/850,288 entitled “Systems and Methods for Globally Optimizing a Process Facility;” U.S. patent application Ser. No. 08/851,590 entitled “Systems and Methods Using Bridge Models to Globally Optimize a Process Facility;” and U.S. patent application Ser. No. 09/137,358 entitled “Controllers that Determine Optimal Tuning Parameters for use in Process Control Systems and Methods of Operating the Same,” all of which are commonly owned by the assignee of the present invention and incorporated herein above by reference for all purposes.
Generally speaking, one problem is that conventional efforts, when applied to specific processes, tend to be non-cooperative (e.g., non-global, non-facility wide, etc.) and may, and all too often do, detrimentally impact the efficiency of the process facility as a whole. For instance, many MPC techniques control process variables to predetermined set points. Oftentimes the set points are a best estimate of a value of the set point or set points. When a process is being controlled to a set point, the controller may not be able to achieve the best control performances, especially under process/model mismatch.
To further enhance the overall performance of a control system, it is desirable to design a controller that deals explicitly with plant or model uncertainty. The '420 Patent, for example, teaches methods of designing a controller utilizing range control. The controller is designed to control a “worst case” process. An optimal controller for the process is achieved and, if the actual process is not a “worst case process,” the performance of the controller is better than anticipated.
There are a number of well known PID “tuning” formulas, or techniques, and the most common, or basic, PID algorithm includes three known user specified tuning parameters (K, &tgr;
1
, &tgr;
2
) whose values determine how the controller will behave. These parameters are determined either by trial and error or through approaches that require knowledge of the process. Although many of these approaches, which are commonly algorithms, have provided improved control, PID controller performance tuned by such algorithms usually degrades as process conditions change, requiring a process engineer, or operator, to monitor controller performance. If controller performance deteriorates, the process engineer is required to “re-tune” the controller.
Controller performance deteriorates for many reasons, although the most common cause is changing dynamics of the process. Since PID controller performance has been related to the accuracy of the process model chosen, a need exists for PID controllers that allows for such uncertainty by accounting for changing system dynamics. Further, the requirement for ever-higher performance control systems demands that system hardware maximize software performance. Conventional control system architectures are made up of three primary components: (i) a processor, (ii) a system memory and (iii) one or more input/output devices. The processor controls the system memory and the input/output (“I/O”) devices. The system memory stores not only data, but also instructions that the processor is capable of retrieving and executing to cause the control system to perform one or more desired functions. The I/O devices are operative to interact with an operator through a graphical user interface, and with the facility as a whole through a network portal device and a process interface.
Over the years, the quest for ever-increasing process control system speeds has followed different directions. One approach to improve control system performance is to increase the rate of the clock that drives the system hardware. As the clock rate increases, however, the system hardware's power consumption and temperature also increase. Increased power consumption is expensive and high circuit temperatures may damage the process control system. Further, system hardware clock rate may not increase beyond a threshold physical speed at which signals may be processed. More simply stated, there is a practical maximum to the clock rate that is acceptable to conventional system hardware.
An alternate approach to improve process control system performance is to increase the number of instructions executed per clock cycle by the system processor (“processor throughput”). One technique
Davis Munck
Gain, Jr. Edward F.
Todd Voeltz Emanuel
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