Linear parameter-varying model to design control laws for an artificial pancreas

Abstract

The contribution of this work is the generation of a control-oriented model for insulin-glucose dynamic regulation in Type 1 Diabetes Mellitus (T1DM). The novelty of this model is that it includes the time-varying nature, and the inter-patient variability of the glucose-control problem. In addition, the model is well suited for well-known and standard controller synthesis procedures. The outcome is an average Linear Parameter-Varying (LPV) model that captures the dynamics from the insulin delivery input to the glucose concentration output constructed based on the UVA/Padova metabolic simulator. Finally, a system-oriented reinterpretation of the classical ad-hoc 1800 rule is applied to adapt the model's gain.The effectiveness of this approach is quantified both in open-and closed-loop. The first one by computing the Root Mean Square Error (RMSE) between the glucose deviation predicted by the proposed model and the UVA/Padova one. The second measure is determined by using the ν-gap as a metric to determine distance, in terms of closed-loop performance, between both models. For comparison purposes, both open-(RMSE) and closed-loop (ν-gap metric) quality indicators are also computed for other control-oriented models previously presented.This model allows the design of LPV controllers in a straightforward way, considering its affine dependence on the time-varying parameter, which can be computed in real-time. Illustrative simulations are included. In addition, the presented modeling strategy was employed in the design of an Artificial Pancreas (AP) control law that successfully withstood rigorous testing using the UVA/Padova simulator, and that was subsequently deployed in a clinical trial campaign where five adults remained in closed-loop for 36 hours. This was the first ever fully closed-loop clinical AP trial in Argentina, and the modeling strategy presented here is considered instrumental in resulting in a very successful clinical outcome.

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

“…Many control solutions are under development that can be used for various kinds of control problems. Advanced control methods have been successfully applied for physiological regulation problems, for example, control of anesthesia [2,3], angiogenic inhibition of cancer [4,5], immune response in presence of human immunodeficiency virus [6], and regulation of blood glucose (BG) level [7][8][9][10] as well.…”

“…Moreover, several clinical trials have been done by using this methodology and investigate its effectivity [20][21][22]. Linear Parameter Varying (LPV) modelbased solutions have high importance, since the uncertainties can be handled with high efficiency by them [7,9,23]. The Tensor Product (TP) model-based techniques also represent interesting directions, since they can be combined by Linear Matrix Inequality (LMI) based control and LPV methodology as well [24,25].…”

Robust guaranteed‐cost output feedback control of blood glucose in type 1 diabetes patient with intrapatient variability

“…For carrying out the simulation of the uncertain system (33) where the controller gain matrix, K is computed using LMIs (37) to (39) and the optimal observer gain matrix by LMI in (31). The values of the controller and observer gain The choice of the constant matrices, D and E is required for the feasibility of the LMI conditions.…”

“…Large variants of control techniques exist for the control of blood glucose of T1DM patients for different patient models. Some of the relevant literature review is presented here, proportional integral derivative, 13 model predictive control, 27,28 fuzzy logic control, 29 adaptive control, 9,[30][31][32] and so on. These control techniques have been successfully and extensively implemented for the considered problem without addressing intrapatient variability issue explicitly in the controller design.…”

Mesterséges hasnyálmirigy – A cukorbetegség automatikus kezelése mérnöki megközelítéssel

“…Egy érdekes példa a lineáris változó paraméterű (LPV) módszertan alkalmazása, ami bizonyíthatóan képes elérni a szabályozási célt fennálló zavarok esetén is. 41,42,43 Egy másik ígéretes lehetőség az LPV kombinálása a lineáris mátrix egyenlőtlenség (LMI) alapú módszerekkel. 44 A legújabb irány ezen módszertan keretein belül a tenzorszorzat (TP) transzformáción alapuló LMI szabályozó tervezés, azonban ez a módszer még nem lett validálva a gyakorlatban.…”