IPC분류정보
국가/구분 |
United States(US) Patent
등록
|
국제특허분류(IPC7판) |
|
출원번호 |
UP-0678634
(2007-02-26)
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등록번호 |
US-7599749
(2009-10-20)
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발명자
/ 주소 |
- Sayyarrodsari, Bijan
- Hartman, Eric
- Axelrud, Celso
- Liano, Kadir
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출원인 / 주소 |
- Rockwell Automation Technologies, Inc.
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대리인 / 주소 |
|
인용정보 |
피인용 횟수 :
21 인용 특허 :
13 |
초록
▼
The present invention provides a method for controlling nonlinear control problems within particle accelerators. This method involves first utilizing software tools to identify variable inputs and controlled variables associated with the particle accelerator, wherein at least one variable input para
The present invention provides a method for controlling nonlinear control problems within particle accelerators. This method involves first utilizing software tools to identify variable inputs and controlled variables associated with the particle accelerator, wherein at least one variable input parameter is a controlled variable. This software tool is further operable to determine relationships between the variable inputs and controlled variables. A control system that provides variable inputs to and acts on controller outputs from the software tools tunes one or more manipulated variables to achieve a desired controlled variable, which in the case of a particle accelerator may be realized as a more efficient collision.
대표청구항
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The invention claimed is: 1. A system for controlling a non-linear process, comprising: a distributed control system operable to couple to a non-linear process with varying dynamics, wherein dynamic behavior of the non-linear process varies as a function of process operation regime, the distributed
The invention claimed is: 1. A system for controlling a non-linear process, comprising: a distributed control system operable to couple to a non-linear process with varying dynamics, wherein dynamic behavior of the non-linear process varies as a function of process operation regime, the distributed control system comprising: a computing device operable to execute a first software tool to: identify variable inputs and controlled variables associated with the non-linear process, wherein the variable inputs include at least one manipulated variable; determine relationships between the variable inputs and the controlled variables; and construct a dynamic predictive model of the non-linear process that expresses the determined relationships between the variable inputs and the controlled variables, wherein the dynamic predictive model comprises parameters that are functionally dependent on and vary with the variable inputs; and at least one input/output controller coupled to the computing device, operable to monitor the variable inputs and tune the at least one manipulated variable based on the determined relationships to achieve a desired controlled variable value. 2. The system of claim 1, wherein the functional dependence of the parameters of the dynamic predictive model is defined by one or more of: an explicit functional description; an empirical model; and a tabular model. 3. The system of claim 2, wherein the empirical model comprises a neural network. 4. The system of claim 1, wherein the dynamic predictive model comprises a first principles model, wherein the first principle model is dependent on the variable inputs. 5. The system of claim 1, wherein the dynamic predictive model comprises a state-space representation of the non-linear process with varying dynamics, wherein the state-space representation is dependent on the variable inputs. 6. The system of claim 1, wherein the dynamic predictive model comprises a combination of at least one physical model and at least one empirical model. 7. The system of claim 6, wherein the at least one physical model and the at least one empirical model are combined in series. 8. The system of claim 6, wherein the at least one physical model and the at least one empirical model are combined in parallel. 9. The system of claim 6, wherein the at least one physical model varies over an operating range. 10. The system of claim 6, wherein the at least one physical model comprises first principle parameters that vary with the variable inputs, wherein the at least one empirical model comprises a neural network operable to determine first principle parameter values associated with the variable inputs, and wherein the neural network updates the first principle parameters with the determined first principle parameter values. 11. The system of claim 10, wherein the neural network is trained, and wherein the neural network is trained according to at least one method selected from the group consisting of: gradient methods, back propagation, gradient-based nonlinear programming methods, sequential quadratic programming, generalized reduced gradient methods, and non-gradient methods. 12. The system of claim 11, wherein gradient methods require gradients of an error with respect to a weight and bias obtained by one or more of: numerical derivatives; or analytical derivatives. 13. The system of claim 1, wherein the first software tool comprises an empirical model. 14. The system of claim 1, wherein the first software tool comprises a combination of at least one physical model and at least one empirical model, wherein the at least one physical model and the at least one empirical model are combined in one of: series; or parallel. 15. The system of claim 4, wherein the at least one physical model is a function of the variable inputs and varies over an operating range. 16. The system of claim 14, wherein the at least one physical model comprises first principle parameters that vary with the variable inputs, wherein the at least one empirical model comprises a neural network used to identify first principle parameters associated with the variable inputs, and determine relationships between the first principle parameters and the variable inputs. 17. The system of claim 16, wherein the neural network is trained, and wherein the neural network is trained according to at least one method selected from the group consisting of: gradient methods, back propagation, gradient-based nonlinear programming methods, sequential quadratic programming, generalized reduced gradient methods, and non-gradient methods. 18. The system of claim 17, wherein gradient methods require gradients of an error with respect to a weight and bias obtained by one or more of: numerical derivatives; or analytical derivatives. 19. A dynamic controller for controlling a non-linear process, comprising: a dynamic predictive model of a non-linear process with varying dynamics, wherein dynamic behavior of the non-linear process varies as a function of process operation regime, wherein the dynamic predictive model is operable to predict a change in at least one dynamic variable input value to the non-linear process to effect a change in at least one output of the non-linear process from a current output value at a first time to a desired output value at a second time, wherein the dynamic predictive model comprises: a steady state component; and a dynamic component; wherein the dynamic predictive model is operable to: receive a current variable input value for the non-linear process, wherein both the steady state component and the dynamic component of the dynamic predictive model change dependent upon the received current variable input value; and determine a plurality of desired controlled variable values at a plurality of different times between the first time and the second time to define a dynamic operation path for the non-linear process between the current output value and the desired output value at the second time; and an optimizer, coupled to the dynamic predictive model, wherein the optimizer is operable to: optimize operation of the dynamic controller over the plurality of different times in accordance with a predetermined optimization method to achieve a desired path for the non-linear process from the first time to the second time. 20. A method for controlling a non-linear process, comprising: providing a dynamic controller for controlling a non-linear process, wherein the dynamic controller comprises a dynamic predictive model of a non-linear process with varying dynamics, wherein dynamic behavior of the non-linear process varies as a function of process operation regime, wherein the dynamic predictive model is operable to predict a change in at least one dynamic variable input value to the non-linear process to effect a change in at least one output of the non-linear process from a current output value at a first time to a desired output value at a second time, wherein the dynamic predictive model comprises: a steady state component; and a dynamic component; and an optimizer, coupled to the dynamic predictive model; the dynamic predictive model receiving a current variable input value for the non-linear process, wherein both the steady state component and the dynamic component of the dynamic predictive model change dependent upon the received current variable input value; the dynamic predictive model determining a plurality of desired controlled variable values at a plurality of different times between the first time and the second time to define a dynamic operation path for the non-linear process between the current output value and the desired output value at the second time; and the optimizer optimizing operation of the dynamic controller over the plurality of different times in accordance with a predetermined optimization method to achieve a desired path for the non-linear process from the first time to the second time.
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