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An Efficient model based control Algorithm for the Determination of an Optimal control Policy for a Constrained Stochastic Linear System

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IFAC-PapersOnLine 2018年第18期51卷 590-595页

作者： Prakash, J. Zamar, David Gopaluni, Bhushan Kwok, Ezra Department of Instrumentation Engineering Madras Institute of Technology Campus Anna University Chennai India Department of Chemical and Biological Engineering University of British Columbia Canada

In this paper, the authors have proposed an ensemble Kalman filter based stochastic model predictive control algorithm to determine an optimal control policy at every sampling time instant for a constrained stochastic linear system. To determine an optimal control policy for the constrained linear system affected by random disturbances and measurements corrupted by random noise, the authors have minimized the uncertain objective function, subject to uncertain state & output constraints and deterministic input constraints using the quantile based scenario analysis approach. In this work, ensemble Kalman filter is being employed, to generate a recursive estimate of states of the constrained stochastic linear system. The number of scenarios is considered to be equivalent to that of number of sample points used in the ensemble Kalman filter. Each scenario is viewed as one realization of the process noise, measurement noise over the prediction horizon as well as the ith sample point of the state estimate at the beginning of the prediction horizon generated by the ensemble Kalman filter. Simulation studies have been carried out to assess the efficacy of the proposed control scheme on the simulated model of the constrained single-input and single-output linear stochastic system. © 2018

关键词： Stochastic models Kalman filters Linear systems model predictive control Predictive control systems Stochastic control systems Stochastic systems Constrained linear systems Ensemble Kalman Filter model based control algorithms Scenario analysis Scenario analysis approach Scenario optimizations Single input and single outputs Stochastic linear systems

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The Contribution of Physical Activity in Blood Glucose Concentration for People with Type 1 Diabetes

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IFAC-PapersOnLine 2018年第27期51卷 270-275页

作者： Boiroux, Dimitri Jørgensen, John Bagterp Patek, Stephen D. Breton, Marc D. Department of Applied Mathematics and Computer Science Technical University of Denmark LyngbyDK-2800 Kgs Denmark Department of Systems and Information Engineering University of Virginia Charlottesville Virginia United States Center for Diabetes Technology University of Virginia CharlottesvilleVA United States

This paper addresses the problem of mathematical deconvolution for the estimation of unknown inputs in linear discrete-time state-space models. We apply our deconvolution algorithm to the modeling of blood glucose (BG) concentration for people with type 1 diabetes (T1D). We present a method using an activity tracking watch, a continuous glucose monitor and an insulin pump to study the effect of physical activity on BG concentration for people with T1D. The physical activity signatures are represented by an unknown input, also referred to as a ”net effect”. In addition, the net effect captures the unmodelled BG variations, eg. mismatches in meal estimation, and circadian metabolic variations. We test our method using data from a clinical study. We show the glucose net effect traces associated to physical activity for a specific patient during 20 consecutive days, and the glucose net effect traces associated to physical activity for eight subjects under identical conditions. The net effect signatures can be used to (i) reproduce experiments with different insulin administration strategies, (ii) build a physiological model of glucose-insulin dynamics able to simulate a physical activity in people with T1D, and (iii) design a model-based control algorithm able to predict the effect of physical activity on the blood glucose concentration. © 2018

关键词： Glucose Blood Deconvolution Estimation Insulin Physiological models State space methods Blood glucose concentration Deconvolution algorithm Discrete time state space models Disturbance estimation model based control algorithms Net effect Physical activity Type 1 diabetes

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