State based adaptive feedback feedforward PID controller
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G05B-013/02
G06F-007/60
G06F-017/10
출원번호
UP-0534943
(2006-09-25)
등록번호
US-7551969
(2009-07-01)
발명자
/ 주소
Wojsznis, Wilhelm K.
Blevins, Terrence L.
출원인 / 주소
Fisher Rosemount Systems, Inc.
대리인 / 주소
Marshall, Gerstein & Borun LLP
인용정보
피인용 횟수 :
10인용 특허 :
30
초록▼
A state based adaptive feedback/feedforward PID controller includes a model set component, communicatively coupled to a process input, having a state variable defining a number of process regions, and a number of models grouped into the process regions. Each of the grouped models includes a pluralit
A state based adaptive feedback/feedforward PID controller includes a model set component, communicatively coupled to a process input, having a state variable defining a number of process regions, and a number of models grouped into the process regions. Each of the grouped models includes a plurality of parameters having a value selected from a set of predetermined initial values assigned to the respective parameter. The adaptive controller further includes an error generator communicatively coupled to the model set component and a process output. The error generator configured to generate a model error signal representative of the difference between a model output signal and a process output signal. The error generator, communicatively coupled to a model evaluation component, is configured to compute a model squared error corresponding to a model and correlating the model squared error to parameter values represented in the model. The adaptive controller further includes a parameter interpolator communicatively coupled to the model evaluation component for calculating a respective adaptive parameter value for parameters represented in the model and a controller update component, communicatively coupled to the parameter interpolator, for updating the controller in response to adaptive parameter values upon conclusion of an adaptation cycle.
대표청구항▼
What is claimed is: 1. A method of tuning a process controller for use in controlling a controlled process, comprising: defining a multiplicity of model parameter values for each of a plurality of model parameters associated with a generic process model, the generic process model being representati
What is claimed is: 1. A method of tuning a process controller for use in controlling a controlled process, comprising: defining a multiplicity of model parameter values for each of a plurality of model parameters associated with a generic process model, the generic process model being representative of the controlled process; creating a set of individual process models, wherein each of the set of individual process models is formed from the generic process model using one of the multiplicity of model parameter values for each of the plurality of model parameters, and wherein each of the set of individual process models is formed using a different combination of model parameter values for the model parameters; performing a model scan, including; executing each of the individual process models on one or more process inputs to produce a model output for each of the individual process models, comparing the output of each of the individual process models to a process output to determine a model error value for each of the individual process models, for each individual process model, associating the model error value for the individual process model with each of the model parameter values used in that individual process model, and for each model parameter value of each model parameter, computing a model parameter value norm from the model error values associated with that model parameter value; for each model parameter, determining a new model parameter value from the model parameter value norms computed for the multiplicity of parameter values for that model parameter, to create a set of new model parameter values including one new model parameter value for each of the model parameters; and determining one or more process controller tuning values from the set of new model parameter values. 2. The method of claim 1, further comprising performing an adaptation cycle by performing two or more model scans at different times to produce a model parameter value norm for each model parameter value associated with each model scan, and wherein determining a new model parameter value for a particular model parameter includes combining the model parameter value norms associated with each of the two or more model scans for each model parameter value of the particular model parameter. 3. The method of claim 2, wherein combining the model parameter value norms for each model parameter value of the particular model parameter includes summing the model parameter value norms created for a particular model parameter value from each of the two or more model scans. 4. The method of claim 3, wherein determining the new model parameter value for each model parameter includes, for a particular model parameter, computing a weighting value for each model parameter value of the particular model parameter from the sum of the model parameter value norms for each of the model parameter values for the particular model parameter and using the weighting value for each of the model parameter values for the particular model parameter to determine the new model parameter value for the particular model parameter. 5. The method of claim 4, wherein determining the weighting value for one of the model parameter values of the particular model parameter includes inverting the sum of the model parameter value norms for the one of the model parameter values. 6. The method of claim 4, wherein defining the multiplicity of model parameter values for each of the plurality of model parameters associated with the generic process model includes defining one of the multiplicity of model parameter values for one of the model parameters as the new model parameter value for the one of the model parameters determined in a previous adaptation cycle. 7. The method of claim 6, wherein defining the multiplicity of model parameter values for each of the plurality of model parameters associated with the generic process model includes defining others of the multiplicity of model parameter values for the one of the model parameters as a function of the new model parameter value for the one of the model parameters determined in a previous adaptation cycle. 8. The method of claim 1, wherein comparing the output of each of the individual process models to the process output to determine the error value for each of the individual process models including determining a squared error between the output of each of the individual process models and the process output to determine the model error value for each of the individual process models. 9. The method of claim 1, wherein computing the model parameter value norm for a particular model parameter value includes summing the model error values associated with the particular model parameter value. 10. The method of claim 1, further including providing the one or more process controller tuning values to the process controller for use in controlling the controlled process. 11. A tuning system for use in tuning a process controller of a controlled process, comprising: a storage device to store a generic process model representative of the controlled process; a storage device to store a multiplicity of model parameter values for each of a plurality of model parameters associated with the generic process model; and a tuning system including, a model creation routine stored in a memory to be executed on a processor to create a set of individual process models, wherein each of the individual process models is formed from the generic process model using one of the multiplicity of model parameter values for each of the plurality of model parameters, and wherein each of the set of individual process models is formed using a different combination of the model parameter values for the model parameters; a model execution routine stored in the memory to be executed on a processor to perform one or more model scans, each model scan including; executing each of the individual process models on one or more process inputs to produce a model output for each of the individual process models, comparing the output of each of the individual process models to a process output to determine a model error value for each of the individual process models; for each individual process model, associating the model error value for the individual process model with each of the model parameter values used in that individual process model; and for each model parameter value of each model parameter, computing a model parameter value norm from the model error values associated with that model parameter value; a model parameter value determining routine stored in the memory to be executed on a processor to determine a new model parameter value for each of the model parameters, wherein the new model parameter value for a particular model parameter is created from the model parameter value norms determined for the multiplicity of parameter values for the particular model parameter; and a tuning parameter routine stored in the memory to be executed on a processor to determine one or more process controller tuning values to be used by the process controller from the new model parameter values for the model parameters. 12. The tuning system of claim 11, wherein the model execution routine performs a model adaptation cycle by performing two or more model scans at different times to produce a model parameter value norm for each model parameter value during each of the two or more model scans, and wherein the model parameter value determining routine determines a new model parameter value for a particular model parameter by combining the model parameter value norms determined during the two or more model scans of the adaptation cycle for the model parameter values of the particular model parameter, to produce the new model parameter value for the particular model parameter. 13. The tuning system of claim 12, wherein the model execution routine combines the model parameter value norms for a particular one of the model parameter values of the particular one of the model parameters by summing the model parameter value norms created for the particular model parameter value developed during each of the two or more model scans. 14. The tuning system of claim 13, wherein the model parameter value determining routine determines the new model parameter value for the particular model parameter by computing a weighting value for each model parameter value of the particular model parameter from the sum of the model parameter value norms for each of the model parameter values of the particular model parameter and using the weighting values for each of the model parameter values for the particular model parameter to determine the new model parameter value for the particular model parameter. 15. The tuning system of claim 14, wherein the model parameter value determining routine computes the weighting value for a particular model parameter value of the particular model parameter by inverting the sum of the model parameter value norms for the particular model parameter value of the particular model parameter. 16. The tuning system of claim 14, wherein the model creation routine defines the multiplicity of model parameter values for each of the plurality of model parameters associated with the generic process model by defining at least one of the multiplicity of model parameter values for one of the model parameters as the new model parameter value for the one of the model parameters determined in a previous adaptation cycle. 17. The tuning system of claim 16, wherein the model creation routine defines the multiplicity of model parameter values for each of the plurality of model parameters associated with the generic process model by defining others of the multiplicity of model parameter values for the one of the model parameters as a function of the new model parameter value for the one of the model parameters determined in a previous adaptation cycle. 18. The tuning system of claim 11, wherein the model execution routine determines the model error value for one of the individual process models as a squared error between the output of the one of the individual process models and the process output. 19. The tuning system of claim 11, wherein the model execution routine computes the model parameter value norm for a particular model parameter value by summing the model error values associated with the particular model parameter value. 20. The tuning system of claim 11, further including a state machine routine stored in the memory to be executed on a processor to monitor one or more process variables of the controlled process to determine when the controlled process moves from one predefined state to another predefined state, and that implements the tuning routine when the controlled process moves from the one predefined state to the another predefined state.
연구과제 타임라인
LOADING...
LOADING...
LOADING...
LOADING...
LOADING...
이 특허에 인용된 특허 (30)
Sunil C. Shah, Adaptation to unmeasured variables.
Keeler, James D.; Hartman, Eric J.; Godbole, Devendra B.; Piche, Steve; Arbila, Laura; Ellinger, Joshua; Ferguson, II, R. Bruce; Krauskop, John; Kempf, Jill L.; O'Hara, Steven A.; Strauss, Audrey; Te, Automated method for building a model.
Kayama Masahiro,JPX ; Kumayama Jiro,JPX ; Fukuoka Shohei,JPX ; Yoshida Masato,JPX ; Sugita Yoichi,JPX ; Morooka Yasuo,JPX, Control model modeling support system and a method therefor.
Heyob Jeffrey J. (Beavercreek OH) Patterson Oliver D. (Beavercreek OH) LeClair Steven R. (Spring Valley OH) Haas T. Walter (Kettering OH) Currie Kenneth (Cookeville TN) Moore Doug (Okeana OH) Adams S, Hierarchical control system for molecular beam epitaxy.
Nasr Hatem N. (Edina MN) Sadjadi Firooz A. (St. Anthony MN) Bazakos Michael E. (Bloomington MN) Amehdi Hossien (Edina MN), Knowledge and model based adaptive signal processor.
Ho Weng K. (Singapore SGX) Hang Chang C. (Singapore SGX) Wojsznis Wilhelm K. (Round Rock TX), Method and apparatus for determining the ultimate gain and ultimate period of a controlled process.
Klaus Weinzierl DE, Method for generating control parameters from a response signal of a controlled system and system for adaptive setting of a PID controller.
Mozumder Purnendu K. (Plano TX) Saxena Sharad (Dallas TX) Pu William W. (Plano TX), Multi-variable statistical process controller for discrete manufacturing.
Shigemasa Takashi (Yokohama JPX) Ichikawa Yoshinori (Yokohama JPX), Process control apparatus with process dependent switching between process control modes.
Gibby Gordon L. (Gainesville FL) Lampotang Samsun (Gainesville FL) Hathiram Daraius (Houston TX) Gravenstein Nikolaus (Gainesville FL), System and method for in-line heating of medical fluid.
Wojsznis Wilhelm K. (Round Rock TX), Variable horizon predictor for controlling dead time dominant processes, multivariable interactive processes, and proces.
Beveridge, Robert Allen; Whalen, Jr., Richard J., Dynamic matrix control of steam temperature with prevention of saturated steam entry into superheater.
Mehta, Ashish; Wojsznis, Peter; Lewis, Marty J.; Jundt, Larry O.; Pettus, Nathan W., Method and apparatus for intelligent control and monitoring in a process control system.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.