IPC분류정보
국가/구분 |
United States(US) Patent
등록
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국제특허분류(IPC7판) |
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출원번호 |
US-0041157
(2002-01-08)
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발명자
/ 주소 |
- Keeler,James David
- Hartman,Eric Jon
- Liano,Kadir
- Ferguson,Ralph Bruce
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출원인 / 주소 |
- Pavilion Technologies, Inc.
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대리인 / 주소 |
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인용정보 |
피인용 횟수 :
5 인용 특허 :
13 |
초록
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A plant is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) models the plant by providing a predicted output which is combined with a desired output to generate an error that
A plant is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) models the plant by providing a predicted output which is combined with a desired output to generate an error that is back propagated through an inverse control network to generate a control error signal that is input to a distributed control system to vary the control inputs to the plant in order to change the output y(t) to meet the desired output. The inverse model represents the dependencies of the plant output on the control variables parameterized by external influences to the plant.
대표청구항
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What is claimed is: 1. A control network for controlling a plant having plant control inputs for receiving plant control variables and desired plant outputs, the plant outputs being a function of the plant control variables and immeasurable external influences on the plant, comprising: a control ne
What is claimed is: 1. A control network for controlling a plant having plant control inputs for receiving plant control variables and desired plant outputs, the plant outputs being a function of the plant control variables and immeasurable external influences on the plant, comprising: a control network input for receiving as network inputs the current plant control variables, measured variables representing non-controllable aspects of the plant and desired plant outputs; a control network output for outputting predicted plant control variables necessary to achieve the desired plant outputs; a processing system for processing the received plant control variables through an inverse representation of the plant that represents the dependencies of the plant output on the plant control variables parameterized by an estimation of the unmeasurable external influences to provide the predicted plant control variables to achieve the desired plant outputs; and an interface device for inputting the predicted plant control variables that are output by said control network output to the plant as updated plant control variables to achieve the desired plant outputs. 2. The control network of claim 1 wherein said processing system further comprises: an estimation network for estimating the external influences on the plant and output estimated external influences; and means for parameterizing the inverse representation of the plant with the estimated influences. 3. The control network of claim 2, wherein said processing system comprises: a first intermediate output for providing a predicted plant output; a first intermediate processing system for receiving the plant control variables from said control network input and the estimated external influences from said estimation network for processing through a predictive model of the plant to generate the predicted plant outputs for output from said intermediate output; an error generation device for comparing the predicted plant outputs to the desired plant outputs and generating an error representing the difference therebetween; a second intermediate processing system for processing the error through the inverse representation of the plant that represents the dependencies of the plant output on the plant control variables parameterized by the estimated external influences to output predicted control variable change values; and a control system for inputting said predicted control variable change values to the input of said first intermediate processing system for summing with the control variable input to provide a summed control variable value, and processing the summed control variable through said first processing system to minimize said error and output the summed control variable value as the predicted control variables. 4. The control network of claim 3, wherein said second intermediate processing system comprises: a neural network having an input layer for receiving said error; an output layer for providing the predicted output of the plant; a hidden layer for mapping said input layer to said output layer through an inverse representation of the plant that represents the dependencies of the plant output on the plant control variables parameterized by the unmeasurable external influences to provide as an output from the output layer the control variable change values. 5. The control network of claim 3 wherein said control system utilizes a gradient descent procedure to minimize said error. 6. The control network of claim 3 wherein said control system utilizes a Monte Carlo technique to minimize said error. 7. The control network of claim 3 wherein said second intermediate processing system and said estimation network comprise: a residual activation neural network having: a residual neural network for receiving as inputs in an input layer the plant control variables and non-manipulatable plant state variables dependant on the plant control variables, and mapping the received plant control variables through a hidden layer to an output layer, the hidden layer having a representation of the dependencies of the plant state variables on the plant control variables to provide as an output from said output layer predicted state variables, a residual layer for determining as a residual the difference between the plant state variables and the predicted state variables as an estimation of the unmeasurable external influences on the plant, and a latch for latching said residual determined in said residual layer after determination thereof; and a main neural network having: an input layer for receiving the plant control variables and said latched residual, an output layer for outputting a predicted plant output, and a hidden layer for mapping said input layer to said output layer through a representation of the plant as a function of the plant control variable inputs and said latched residual, said main neural network operating in an inverse mode to receive at the output layer said error and back propagate said error through said hidden layer to said input layer with said residual latched in said latch to output from said input layer said predicted control variable change values. 8. The control network of claim 1, wherein the inverse representation of said processing system is a general non-linear inverse representation. 9. The control network of claim 1, wherein the control variable inputs are variables that can be manipulated. 10. A network for predicting plant outputs and for receiving control variables and measurable state variables, with the measurable state variables being non-controllable and having dependencies on the control variables, the control network for projecting out the dependencies of the measurable state variables on the control variables and the plant operating in an environment that is affected by unmeasurable external influences, comprising: a residual activation neural network for generating an estimation of external influences on the plant and having: an input layer for receiving the control variables, an output layer for outputting predicted state variables, a hidden layer for mapping said input layer to said output layer through a representation of the dependencies of the state variables on the control variables to generate said predicted state variables, and a residual layer for determining as a residual the difference between said predicted state variables and the input state variables, said residual comprising an estimation of the unmeasurable external influences on the plant; and a main neural network having: an input layer for receiving as inputs the control variables and said residual, an output layer for outputting a predicted plant output, and a hidden layer for mapping said input layer to said output layer through a representation of the plant as a function of the control variables and said residual. 11. The network of claim 10, and further comprising: means for generating an error between the predicted plant output and a desired plant output; a latch for latching said residual in said input layer of said main neural network; and means for operating said main neural network to provide the inverse of said associated representation and back propagate said error through said main neural network from said output layer to the control variable inputs of said input layer of said main neural network to generate predicted control variable change values necessary to achieve said desired plant output. 12. The network of claim 11, wherein said means for generating said error comprises: a predictive model neural network for providing a representation of the plant and for receiving the control variables and the state variables as inputs and predicting the output of the plant as a predicted plant output; and a difference device for receiving said desired plant output and said predicted plant output and generating said error. 13. The network of claim 11, wherein said means for back propagating error through said main neural network comprises means for back propagating error through said main neural network to define said predicted control variable change values, and iteratively summing said change values with the control variables to minimize said error in accordance with a back propagation-to-activation technique. 14. The network of claim 11, wherein said representation stored in said residual activation network is a non-linear representation of the dependency of the state variables on the control variables and the representation in said hidden layer of said main neural network comprises a non-linear representation of the plant output as a function of the input control variables and said residual. 15. A predictive network for predicting the operation of a plant in response to receiving manipulatable control variables and non-manipulatable state variables, the plant operating in an environment where it is affected by external influences that are unmeasurable and uncontrollable, comprising: a residual network for projecting out the dependencies of the state variables on the control variables to generate an estimation of the unmeasurable external influences on the plant and having: an input for receiving input control variables, an output for outputting predicted state variables as a function of the input control variables, a residual processing system for processing the input control variables through a representation of the dependencies of the state variables on the control variables to provide predicted state variables for output by said output, and a residual layer for determining the difference between the input state variables and the predicted state variables, the difference comprising a residual, said residual comprising the estimation of unmeasurable external influences on the plant; and a main network having: an input for receiving as inputs the input control variables and said residual, an output for outputting a predicted output representing the predicted output of the plant, and a main processing system for processing the input control variables and said residual through a representation of the plant as a function of the control variables and said residual. 16. The predictive network of claim 15 wherein said input, said output and said processing system of said residual network comprise a residual neural network having: an input layer for receiving the input control variables; an output layer for outputting said predicted state variables; and a hidden layer for mapping said input layer to said output layer through a non-linear representation of the dependencies of the state variables on the control variables. 17. The predictive network of claim 15 wherein said main network comprises a main neural network having: an input layer for receiving the input control variables and said residual output by said residual layer; an output layer for outputting said predicted output representing the predicted output of the plant; and a hidden layer for mapping said input layer to said output layer through a non-linear representation of the plant as a function of the control variables and said residual. 18. The predictive network of claim 17 wherein said main network has the hidden layer thereof trained through back propagation as a function of known input control variables and residuals from said residual layer, said residuals generated by said residual network, and said output layer of said main network having input thereto known target predicted outputs. 19. A method for controlling a plant having plant outputs and plant control inputs for receiving plant control variables and desired plant outputs, the plant outputs being a function of the plant control variables and unmeasurable external influences on the plant, comprising the steps of: receiving the current plant control variables, plant state variables that represent non-controllable measurable variables of the plant and desired plant outputs; processing the received plant control variables through an inverse representation of the plant that represents the dependencies of the plant output on the plant control variables parameterized by an estimation of the unmeasurable external influences to provide the predicted plant control variables necessary to achieve the desired plant outputs; outputting on an output the predicted plant control variables necessary to achieve the desired plant outputs; and controlling the plant with the predicted plant control variables. 20. The method of claim 19 wherein the inverse representation of the processing system is a general non-linear inverse representation. 21. The method of claim 19 and further comprising; estimating the external influences on the plant as estimated external influences; and parameterizing the inverse representation of the plant with the estimated external influences. 22. The method of claim 21 wherein the step of processing comprises: processing in a first intermediate processing step the plant control variables and the estimated external influences through a predictive model of the plant to generate the predicted plant outputs for output from an intermediate output; comparing the predicted plant outputs to the desired plant outputs and generating an error representing the difference therebetween; and processing in a second intermediate processing step the error through the inverse representation of the plant that represents the dependencies of the plant output on the plant control variables parameterized by the estimated external influences to output predicted control variable change values; and changing the input control variables to the first intermediate step by the control variable change values to provide the predicted plant control variables. 23. The method of claim 22 wherein the second intermediate processing step comprises: receiving on input layer of a neural network the error; mapping the neural network input layer to a neural network output layer through a neural network hidden layer having stored therein a local representation of the plant parameterized by the unmeasurable external influences; and operating the neural network in an inverse relationship wherein the error is received as an input in the output layer and propagated through the hidden layer having a local inverse representation of the plant that represents the dependencies of the plant output on the plant control variables parameterized by the estimated external influences to provide as an output from the neural network input layer the predicted plant control variable change values, wherein the error is back propagated through the neural network hidden layer to the neural network input layer. 24. The method of claim 22 wherein the first intermediate processing step includes the step of estimating and comprises: receiving the plant control variables on an input layer to a residual neural network and mapping the received plant control variables to a residual neural network output layer through a hidden layer, the hidden layer having a representation of the dependencies of non-manipulatable plant state variables on the plant control variables to provide from the output layer predicted state variables as a function of the plant control variables, the residual comprising the estimation of the unmeasurable external influences; determining as a residual the difference between the plant state variables and the predicted state variables; latching the determined residual after determination thereof; receiving the plant control variables and the latched residual on an input layer of a main neural network; and mapping the input layer of the main neural network to an output layer of the main neural network through a main neural network hidden layer having stored therein a representation of the plant as a function of the plant control variable inputs and the residual, to output from the output layer the predicted plant outputs. 25. The method of claim 24 wherein the step of changing the input control variables comprises iteratively changing the input control variables by summing with the predicted control variable change values to minimize the error in accordance with a gradient descent technique.
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