Facultad Regional Reconquista

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Author: search.filters.author.González, Alejandro

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    Stable impulsive zone model predictive control for type 1 diabetic patients based on a long‐term model
    (2020-07-13) González, Alejandro; Rivadeneira, Pablo; Ferramosca, Antonio; Magdelaine, Nicolas; Moog, Claude
    In this work, the problem of regulating blood glucose (glycemia) in type I diabetic patients is studied by means of an impulsive zone model predictive control (iZMPC), which bases its predictions on a novel long‐term glucose‐insulin model. Taking advantage of the impulsive version of the model—which features real‐life properties of diabetes patients that some other popular models do not—the given control guarantees the stability under moderate‐to‐severe plant‐model mismatch and disturbances. Long‐term scenarios—including meals and physiological parameter variations—are simulated and the results are satisfactory as every hyperglycemic and hypoglycemic episodes are suitably controlled.
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    Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance
    (2020-08-01) Abuin, Pablo; Rivadeneira, Pablo; Ferramosca, Antonio; González, Alejandro
    This work presents a pulsatile Zone Model Predictive Control (pZMPC) for the control of blood glucose concentration (BGC) in patients with Type 1 Diabetes Mellitus (T1DM). The main novelties of the algorithm – in contrast to other existing strategies – are: (i) it controls the patient glycemia by injecting short duration insulin boluses for both, the basal and bolus infusions, in an unified manner, (ii) it performs the predictions and estimations (critical to anticipate both, hypo and hyperglycemia) based on a physiological individualized long-term model, (iii) it employs disturbance observers to compensate plant-model mismatches, (iv) it ensures, under standard assumptions, closed-loop stability, and (v) it can be used – under minor modifications – as an optimal basal–bolus calculator to emulate conventional therapies. Because of the latter characteristic, a significantly better performance is achieved, not only in terms of classical indexes (time in the normoglycemia zone, avoidance of hypoglycemia in the short term, avoidance of hyperglycemia in the long term) but also in terms of its applicability (use of the pump or injections). Such a performance is tested in a cohort of in-silico patients from the FDA-accepted UVA/Padova simulation platform, considering the most challenging scenarios.
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    Characterization of SARS-CoV-2 Dynamics in the Host
    (2020-06-01) Abuin, Pablo; Anderson, Alejandro; Ferramosca, Antonio; Hernandez - Vargas; González, Alejandro
    While many epidemiological models were proposed to understand and handle COVID-19 pandemic, too little has been invested to understand human viral replication and the potential use of novel antivirals to tackle the infection. In this work, using a control theoretical approach, validated mathematical models of SARS-CoV-2 in humans are characterized. A complete analysis of the main dynamic characteristic is developed based on the reproduction number. The equilibrium regions of the system are fully characterized, and the stability of such regions is formally established. Mathematical analysis highlights critical conditions to decrease monotonically SARS-CoV-2 in the host, as such conditions are relevant to tailor future antiviral treatments. Simulation results show the aforementioned system characterization.
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    Discrete-time MPC for switched systems with applications to biomedical problems
    (2020-06-23) Anderson, A; González, Alejandro; Ferramosca, Antonio; Hernandez - Vargas, E
    Switched systems in which the manipulated control action is the time-depending switching signal describe many engineering problems, mainly related to biomedical applications. In such a context, to control the system means to select an autonomous system - at each time step - among a given finite family. Even when this selection can be done by solving a Dynamic Programming (DP) problem, such a solution is often difficult to apply, and state/control constraints cannot be explicitly considered. In this work a new set-based Model Predictive Control (MPC) strategy is proposed to handle switched systems in a tractable form. The optimization problem at the core of theMPC formulation consists in an easy-to-solve mixed-integer optimization problem, whose solution is applied in a receding horizon way. Two biomedical applications are simulated to test the controller: (i) the drug schedule to attenuate the effect of viral mutation and drugs resistance on the viral load, and (ii) the drug schedule for Triple Negative breast cancer treatment. The numerical results suggest that the proposed strategy outperform the schedule for available treatments.
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    Impulsive Zone MPC for Type I Diabetic Patients based on a long-term model
    (Ahmad Taher Azar, 2020-02-01) González, Alejandro; Rivadeneira, Pablo; Ferramosca, Antonio; Magdelaine, Nicolas; Moog, Claude
    In this work the problem of regulating glycemia in type I diabetic patients is studied by means of an impulsive zone model predictive control (impulsive ZMPC) based on a novel long-term glucoseinsulin model. Taking advantage of the model - which features real life properties of diabetes patients that some other popular models do not - the proposed control ensures the stability under moderate-tosevere disturbances. A long-term scenario - including meals - are simulated, and the results appear to be satisfactory as long as every hyperglycemia and hypoglycemia episodes are suitably controlled.