Moving-horizon state-initializer for control applications
First Claim
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1. A method of state-estimator for the estimation or initialization of the state of a discrete-time state-space glucose dynamical model based on sensor measurements of output of the model, comprising;
- (a) using moving-horizon optimization to fit a continuous-time function to acquired sensor measurement data-points of each model output,(b) generating a fitted function,(c) subsequently sampling the continuous time function at exactly the sample-period of the state-space dynamic model for which the state is being estimated or initialized, in order to construct a model state via a synthesized output trajectory, and(d) outputting a state estimate of the glucose dynamical model;
whereinsensor re-calibrations are included, by permitting the fitted function to be discontinuous in its value, but not its derivatives, at the point of re-calibration, and wherein the magnitude of the discontinuity is identified by the optimization, and wherein the sampling of the fitted function is performed ignoring the re-calibration discontinuity;
the function fit employs sensor measurement time-stamps and call-time of the state-estimator, wherein;
(i) delays between the sensor and state-estimator are at least partially mitigated by sampling the fitted function backwards in time starting at exactly the estimator call time; and
(ii) sensor data collected at irregular time-intervals, or time-intervals that are not the sample-period of the model for which the state is being estimated or initialized, can be accommodated;
the model for which the state is being estimated or initialized may have inputs, and if inputs are present historical input data is employed in construction of the state;
the state of the model is observable or reconstructible;
when the model for which the state is being estimated or initialized has one single output then the current model state is constructed to reflect the synthesized output trajectory without offset, and when the model for which the state is being estimated or initialized has multiple outputs then a trade-off strategy is employed to reconcile mismatching outputs,wherein the method is adapted for medical device control of drug delivery.
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Abstract
A state-estimator for the estimation or initialization of the state of a discrete-time state-space dynamical model based on sensor measurements of the model output, comprising fitting a continuous-time function to acquired sensor measurement data-points of each model output, and subsequently sampling the continuous time function at exactly the sample-period of the state-space dynamic model for which the state is being estimated or initialized, in order to construct a model state via a synthesized output trajectory.
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Citations
20 Claims
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1. A method of state-estimator for the estimation or initialization of the state of a discrete-time state-space glucose dynamical model based on sensor measurements of output of the model, comprising;
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(a) using moving-horizon optimization to fit a continuous-time function to acquired sensor measurement data-points of each model output, (b) generating a fitted function, (c) subsequently sampling the continuous time function at exactly the sample-period of the state-space dynamic model for which the state is being estimated or initialized, in order to construct a model state via a synthesized output trajectory, and (d) outputting a state estimate of the glucose dynamical model;
whereinsensor re-calibrations are included, by permitting the fitted function to be discontinuous in its value, but not its derivatives, at the point of re-calibration, and wherein the magnitude of the discontinuity is identified by the optimization, and wherein the sampling of the fitted function is performed ignoring the re-calibration discontinuity; the function fit employs sensor measurement time-stamps and call-time of the state-estimator, wherein; (i) delays between the sensor and state-estimator are at least partially mitigated by sampling the fitted function backwards in time starting at exactly the estimator call time; and (ii) sensor data collected at irregular time-intervals, or time-intervals that are not the sample-period of the model for which the state is being estimated or initialized, can be accommodated; the model for which the state is being estimated or initialized may have inputs, and if inputs are present historical input data is employed in construction of the state; the state of the model is observable or reconstructible; when the model for which the state is being estimated or initialized has one single output then the current model state is constructed to reflect the synthesized output trajectory without offset, and when the model for which the state is being estimated or initialized has multiple outputs then a trade-off strategy is employed to reconcile mismatching outputs, wherein the method is adapted for medical device control of drug delivery. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A method of state-estimator for the estimation or initialization of the state of a discrete-time state-space glucose dynamical model based on sensor measurements of output of the model, the method comprising:
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(a) using moving-horizon optimization to fit a continuous-time function to continuous glucose monitoring (CGM) data, (b) generating a fitted function, and (c) outputting a state estimate of the glucose dynamical model, wherein; sensor recalibrations are accommodated by including discontinuity in the glucose output value, but not its derivatives, within the function'"'"'s definition, and wherein the magnitude of the discontinuity is identified by the optimization; data fitting employs the CGM time-stamps and controller call time, thus asynchronous data sampling is handled naturally; after optimization the fitted function is sampled at the controller model'"'"'s sample-period T, ignoring the recalibration discontinuity, to synthesize an output trajectory; and in combination with historical input data, and assuming observability, the current model state is constructed to reflect the fitted output trajectory without offset, wherein the method is adapted for medical device control of drug delivery. - View Dependent Claims (20)
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Specification