Model predictive controller solution analysis process
First Claim
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1. A method of analyzing a solution from a multivariable predictive controller, comprising:
- obtaining a solution from a multivariable predictive controller having a steady-state optimizer that results in different variable constraint statuses, wherein the solution includes controlled variables that are predicted from manipulated variables; and
operating on the solution to obtain a relationship between constrained variables and unconstrained variables to determine how unconstrained variables respond to changes in constrained variables; and
for each constrained variable, determining how far it can be moved until a next constraint is reached; and
using the relationship between the constrained and unconstrained variables to determine an amount of change needed to move a constraint for a violated variable such that the violated variable is no longer constrained into feasibility.
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Abstract
The solution from a multivariable predictive controller (MPC) is analyzed and described by providing quantitative input to operators regarding the effect of changing controller limits on the MPC controller solution. This information allows a rapid operator response to changes and more optimal process operation.
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Citations
31 Claims
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1. A method of analyzing a solution from a multivariable predictive controller, comprising:
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obtaining a solution from a multivariable predictive controller having a steady-state optimizer that results in different variable constraint statuses, wherein the solution includes controlled variables that are predicted from manipulated variables; and operating on the solution to obtain a relationship between constrained variables and unconstrained variables to determine how unconstrained variables respond to changes in constrained variables; and for each constrained variable, determining how far it can be moved until a next constraint is reached; and using the relationship between the constrained and unconstrained variables to determine an amount of change needed to move a constraint for a violated variable such that the violated variable is no longer constrained into feasibility. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method of operating a control system for use with a process facility, comprising:
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extracting a raw gain matrix from a base model file, including manipulated variables and controlled variables related to the process, based on a steady-state response between the manipulated variables and the controlled variables; classifying the manipulated variables and controlled variables by an active constraint condition, wherein the classification is based on constrained, unconstrained or violated conditions; calculating an amount of possible movement for each variable based on the active constraint condition classification; wherein calculating the amount of possible movement for a constrained variable includes determining which constraint condition will be reached next; wherein calculating the amount of possible movement for an unconstrained variable results in an operator high limit, an operator low limit or a step limit; changing the order of the gain matrix based on the active constraint condition classification to obtain a model matrix representative of an optimization solution; forming a result matrix by pivoting the constrained controlled variables with the unconstrained manipulated variables in the model matrix to form the result matrix; and
,using the result matrix and the amount of possible movement to calculate the response of the unconstrained variables to changes in the constrained variables. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14)
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15. A control system for use with a process, comprising:
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a storage device that stores a base model file including manipulated variables and controlled variables related to the process; and a controller associated with the storage device that extracts a raw gain matrix from the base model file based on a steady-state response between the manipulated variables and the controlled variables, wherein the controller uses an optimization solution to describe how unconstrained variables respond to changes in constrained variables by classifying the manipulated variables and controlled variables by an active constraint condition, wherein the classification is based on constrained, unconstrained or violated conditions, calculating an amount of possible movement for each variable based on the active constraint condition classification, wherein the controller calculates the amount of possible movement for a violated variable by determining what movement will return the variable to a limit; changing the order of the raw gain matrix based on the active constraint condition to obtain a model matrix representative of an optimization solution, forming a result matrix by pivoting the constrained controlled variables with the unconstrained manipulated variables in the model matrix to form the result matrix, and using the result matrix and the amount of possible movement to calculate the response of the unconstrained variables to changes in the constrained variables. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23)
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24. A method of operating a control system for use with a process facility, comprising:
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extracting a raw gain matrix from a base model file, including manipulated variables and controlled variables related to the process, based on a steady-state response between the manipulated variables and the controlled variables; classifying the manipulated variables and controlled variables by an active constraint condition, wherein the classification is based on constrained, unconstrained or violated conditions; calculating an amount of possible movement for each variable based on the active constraint condition classification; wherein calculating the amount of possible movement for a constrained variable includes determining which constraint condition will be reached next; wherein calculating the amount of possible movement for a violated variable includes determining what movement will return the variable to a limit; changing the order of the raw gain matrix based on the active constraint condition to obtain a model matrix representative of an optimization solution; forming a result matrix by pivoting the constrained controlled variables with the unconstrained manipulated variables in the model matrix to form the result matrix; and
,using the result matrix and the amount of possible movement to calculate the response of the unconstrained variables to changes in the constrained variables. - View Dependent Claims (25, 26, 27, 28, 29, 30, 31)
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Specification