Closing the Loop

Nimri, Revital; Murray, Nathan E.; Ochs, Alexander; Pinsker, Jordan E.; Dassau, Eyal

doi:10.1089/dia.2017.2504

Cited by 8 publications

(6 citation statements)

References 77 publications

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“…In these days from control point of view the most beneficial approach is the Artificial Pancreas (AP) concept. This idea aims to imitate the regular operation of the pancreas from the insulin production point of view, namely, administering insulin demands on the needs determined by the BG level [19]. Thus, we have to face contradictory requirements: the generalization and personalization as well.…”

Section: Introductionmentioning

confidence: 99%

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Receding Horizon Control of Type 1 Diabetes Mellitus by Using Nonlinear Programming

et al. 2018

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Receding Horizon Controllers are one of the mostly used advanced control solutions in the industry. By utilizing their possibilities we are able to predict the possible future behavior of our system; moreover, we are able to intervene in its operation as well. In this paper we have investigated the possibilities of the design of a Receding Horizon Controller by using Nonlinear Programming. We have applied the developed solution in order to control Type 1 Diabetes Mellitus. The nonlinear optimization task was solved by the Generalized Reduced Gradient method. In order to investigate the performance of our solution two scenarios were examined. In the first scenario, we applied “soft” disturbance—namely, smaller amount of external carbohydrate—in order to be sure that the proposed method operates well and the solution that appeared through optimization is acceptable. In the second scenario, we have used “unfavorable” disturbance signal—a highly oscillating external excitation with cyclic peaks. We have found that the performance of the realized controller was satisfactory and it was able to keep the blood glucose level in the desired healthy range—by considering the restrictions for the usable control action.

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Section: Introductionmentioning

confidence: 99%

“…The most frequently used method is the Model Predictive Control (MPC) regarding the control of DM [19,[26][27][28]. MPC is a widely used approach in various research fields as well [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Receding Horizon Control of Type 1 Diabetes Mellitus by Using Nonlinear Programming

et al. 2018

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“…Sampling time T was chosen to be T = 5 min due to the current blood glucose concentration measuring devices are capable of providing data at similar frequencies [5], [13] with one exception. During our investigations it became clear that the Adams-Bashforth method was not able to provide acceptable outcome with T = 5.…”

Section: Resultsmentioning

confidence: 99%

“…For example, the sampling frequency is around 5 minutes on basis, noise and disturbance sensitivity, inaccuracies of available mathematical models, etc. [2], [5], [14]. State estimation for control applications is also an important aspect which is not trivial and the requirements of it determined by the type of control to be applied [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Discrete LPV Modeling of Diabetes Mellitus for Control Purposes

Eigner

Siket

Szakál

et al. 2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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The utilization of modern and advanced control engineering related methods for the control, estimation and assessment of physiological applications is widespread. It is also well-known that this engineering apparatus is executed on digital computers. The current insufficiency of available and accurate discretized models, especially in case of Diabetes Mellitus (DM), provides incentive for this research. The researchers typically approximate the continuous solutions which may not be the best alternative in many cases, in particular considering numerical stability and cost-effectiveness. In this paper we performed an analysis of the available discretization options in order to develop discrete models with a special focus on the Linear Parameter Varying (LPV) systems. LPV techniques are very useful frameworks which allow the application of linear controller, observer and estimator design. In this study, three LPV discretization and two Jacobian based discretization methods are introduced and analyzed to provide a basis for our further investigations in the topic.

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“…Current continuous glucose monitoring systems (CGMS) achieve 5-minutes-long sampling periods [3]. The sparseness of the measurements is only one obstacle in the control design procedure, others include noises and disturbances or the uncertainty of the mathematical model [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Discrete LPV Based Parameter Estimation For TIDM Patients By Using Dual Extended Kalman Filtering Method

Kovács

Siket

Rudas

et al. 2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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In case of physiological systems state and parameter estimation is a crucial question. It is key to describe given patient population with appropriate accuracy. Furthermore, state feedback kind of applications also require some sort of estimation procedure in order to get internal information about the controlled system. Linear parameter varying (LPV) framework is beneficial for controller design as well. However, to realize the necessary scheduling parameters, estimation of both state variables and model parameters is needed. A possible solution is the application of Dual Extended Kalman Filter (DEKF) which is able to estimate these signals. The developed framework can be used to design LPV based controller in our further work. In this study we introduce our developed DEKF solution by using the widely applied Cambridge Type 1 Diabetes Mellitus (T1DM) model for virtual patient generation. We have found that our solution is able to estimate the state variables with good accuracy. The variation of parameters can also be tracked by using the proposed solution.

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Closing the Loop

Cited by 8 publications

References 77 publications

Receding Horizon Control of Type 1 Diabetes Mellitus by Using Nonlinear Programming

Receding Horizon Control of Type 1 Diabetes Mellitus by Using Nonlinear Programming

Discrete LPV Modeling of Diabetes Mellitus for Control Purposes

Discrete LPV Based Parameter Estimation For TIDM Patients By Using Dual Extended Kalman Filtering Method

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