Model predictive control with uncertainties
First Claim
1. A method for controlling an operation of a machine according to a model of the machine dynamics, wherein the method uses a processor coupled with stored instructions implementing the method, wherein the instructions, when executed by the processor carry out at least some steps of the method, comprising:
- controlling iteratively the operation of the machine with control inputs determined using the model based on an optimization of a cost function subject to constraints on the control inputs, and constraints on the state of the machine, wherein the operation is controlled online over a plurality of iterations, each iteration comprises;
determining a current state of the machine using measurements of outputs of the machine controlled with a previous control input determined for a previous iteration;
updating a parameter of a model of the machine dynamics to reduce a prediction error between the current state and a state estimated using the model of the machine dynamics, and wherein the parameter of the model of the machine dynamics represents a physical quantity of the machine;
optimizing the cost function to produce a control input, wherein the cost function includes a first term related to a performance of the machine and a second term related to improving estimation of the parameter of the model of the machine dynamics, wherein the second term is weighted by a function of the prediction error, and wherein the second term includes an information functional of a predicted error covariance of the parameter of the model of the machine dynamics; and
controlling the machine using the control input.
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Abstract
A method controls iteratively the operation of the machine with control inputs determined using the model of the machine based on an optimization of a cost function subject to constraints on the control inputs. A current iteration of the method includes determining a current state of the machine after the controlling with a previous control input determined for a previous iteration by optimizing a previous cost function using a previous model of the machine and determining a current model of the machine to reduce a difference between the current state and a state estimated using the previous model of the machine. The cost function is updated during the current iteration based on a difference between the previous model and the current model to produce a current cost function. A current control input for the controlling at the current iteration is determined using the current model and the current cost function.
18 Citations
15 Claims
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1. A method for controlling an operation of a machine according to a model of the machine dynamics, wherein the method uses a processor coupled with stored instructions implementing the method, wherein the instructions, when executed by the processor carry out at least some steps of the method, comprising:
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controlling iteratively the operation of the machine with control inputs determined using the model based on an optimization of a cost function subject to constraints on the control inputs, and constraints on the state of the machine, wherein the operation is controlled online over a plurality of iterations, each iteration comprises; determining a current state of the machine using measurements of outputs of the machine controlled with a previous control input determined for a previous iteration; updating a parameter of a model of the machine dynamics to reduce a prediction error between the current state and a state estimated using the model of the machine dynamics, and wherein the parameter of the model of the machine dynamics represents a physical quantity of the machine; optimizing the cost function to produce a control input, wherein the cost function includes a first term related to a performance of the machine and a second term related to improving estimation of the parameter of the model of the machine dynamics, wherein the second term is weighted by a function of the prediction error, and wherein the second term includes an information functional of a predicted error covariance of the parameter of the model of the machine dynamics; and controlling the machine using the control input. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method for controlling an operation of a machine according to a model of the machine dynamics including a nominal model defining relationships among parameters of the model and an uncertainty model defining a range of values for at least one parameter of the model, wherein the method uses a processor coupled with stored instructions implementing the method, wherein the instructions, when executed by the processor carry out at least some steps of the method, comprising:
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controlling iteratively the operation of the machine with control inputs determined using the model of the machine dynamics based on an optimization of a cost function, wherein the optimization is subject to control-invariant constraints on the operation of the machine including constraints on the control inputs and the state of the machine, selected such that any value of the control input satisfying the control-invariant constraints maintains a state of the machine in a control-invariant subset of states satisfying constraints on the operation of the machine, wherein for any state of the machine within the control-invariant subset there is an admissible control input satisfying the control-invariant constraints and maintaining the state of the machine within the control-invariant subset for all values of the parameters of the model within the range defined by the uncertainty model, wherein the parameter of the model of the machine dynamics represents a physical quantity of the machine, wherein the operation is controlled online over a plurality of iterations, each iteration comprises; determining a current state of the machine using measurements of outputs of the machine controlled with a previous control input determined for a previous iteration; updating a parameter of a model of the machine dynamics to reduce a prediction error between the current state and a state estimated using the model of the machine dynamics, such that the updated value of the parameter is within the range of values; optimizing the cost function to produce a control input, wherein the cost function includes a first term related to a performance of the machine and a second term related to improving estimation of the parameter of the model of the machine dynamics, wherein the second term is weighted by a function of the prediction error, and wherein the second term includes an information functional of a predicted error covariance of the parameter of the model of the machine dynamics; and controlling the machine using the control input.
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14. A control system for controlling iteratively an operation of a machine according to a model of the machine dynamics, comprising:
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a memory to store the model of the machine including a nominal model defining relationships among parameters of the model and an uncertainty model defining a range of values for at least one parameter of the model, and the constraints on the operation of the machine including constraints on the control inputs and the state of the machine, wherein the parameter of the model of the machine dynamics represents a physical quantity of the machine; and at least one processor to determine a current state of the machine using measurements of outputs of the machine; update the parameter of the model of the machine dynamics to reduce a prediction error between the current state and a state estimated using the model of the machine dynamics, such that the updated value of the parameter is within the range of values; optimize the cost function to produce a control input, wherein the cost function includes a first term related to a performance of the machine and a second term related to improving estimation of the parameter of the model of the machine dynamics, wherein the second term is weighted by a function of the prediction error, and wherein the second term includes an information functional of a predicted error covariance of the parameter of the model of the machine dynamics; and control the machine using the control input. - View Dependent Claims (15)
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Specification