156 research outputs found

    LPV-based control of nonlinear compartmental model with input uncertainty

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    LPV-based control of nonlinear compartmental model with input uncertainty

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    Applicability Results of a Nonlinear Model-Based Robust Blood Glucose Control Algorithm

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    INTRODUCTION: Generating optimal control algorithms for an artificial pancreas is an intensively researched problem. The available models are all nonlinear and rather complex. Model predictive control or run-to-run-based methodologies have proven to be efficient solutions for individualized treatment of type 1 diabetes mellitus (T1DM). However, the controller has to ensure safety and stability under all circumstances. Robust control methods seek to provide this safety and guarantee to handle even the worst-case situations and, hence, to generalize and complement results obtained by individualized control algorithms. METHODS: Modern robust (e.g., H(inf)) control is a linear model-based methodology that we have combined with the nonlinear model-based linear parameter varying technique. The control algorithm was designed on the high-complexity modified nonlinear glucose–insulin model of Sorensen, and it was compared step-by- step with linear model-based H(inf) control results published in the literature. The applicability of the developed algorithm was tested first on a control cohort of 10 healthy persons’ oral glucose tolerance test results and then on a large meal absorption profile adapted from the literature. In the latter case, two preliminary virtual patients were generated based on 1–1 week real continuous glucose monitor measurements. RESULTS: We have found that the algorithm avoids hypoglycemia (not caused by physical activity or stress) independently from the considered absorption profiles. CONCLUSION: Use of hard constraints proved their efficiency in fitting blood glucose level within a defined interval. However, in the future, more data of different T1DM patients will be collected and tested, including dynamic absorption model and in silico tests on validated simulators

    Modelling, Optimisation and Explicit Model Predictive Control of Anaesthesia Drug Delivery Systems

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    The contributions of this thesis are organised in two parts. Part I presents a mathematical model for drug distribution and drug effect of volatile anaesthesia. Part II presents model predictive control strategies for depth of anaesthesia control based on the derived model. Closed-loop model predictive control strategies for anaesthesia are aiming to improve patient's safety and to fine-tune drug delivery, routinely performed by the anaesthetist. The framework presented in this thesis highlights the advantages of extensive modelling and model analysis, which are contributing to a detailed understanding of the system, when aiming for the optimal control of such system. As part of the presented framework, the model uncertainty originated from patient-variability is analysed and the designed control strategy is tested against the identified uncertainty. An individualised physiologically based model of drug distribution and uptake, pharmacokinetics, and drug effect, pharmacodynamics, of volatile anaesthesia is presented, where the pharmacokinetic model is adjusted to the weight, height, gender and age of the patient. The pharmacodynamic model links the hypnotic depth measured by the Bispectral index (BIS), to the arterial concentration by an artificial effect site compartment and the Hill equation. The individualised pharmacokinetic and pharmacodynamic variables and parameters are analysed with respect to their influence on the measurable outputs, the end-tidal concentration and the BIS. The validation of the model, performed with clinical data for isoflurane and desflurane based anaesthesia, shows a good prediction of the drug uptake, while the pharmacodynamic parameters are individually estimated for each patient. The derived control design consists of a linear multi-parametric model predictive controller and a state estimator. The non-measurable tissue and blood concentrations are estimated based on the end-tidal concentration of the volatile anaesthetic. The designed controller adapts to the individual patient's dynamics based on measured data. In an alternative approach, the individual patient's sensitivity is estimated on-line by solving a least squares parameter estimation problem.Open Acces

    Population pharmacokinetic modelling to address the gaps in knowledge of commonly used HIV and TB drugs in children

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    The epidemiology of HIV and TB are overlapping, particularly in sub-Saharan Africa, and TB infection remains common in HIV-positive children. The combined administration of anti-tubercular and antiretroviral therapies(ART) may lead to drug-drug interactions potentially needing to be addressed with the adjustment of doses. This thesis assessed the pharmacokinetics of abacavir and ethambutol and evaluated the influence of covariates such as age and concomitant medication on the PK parameters across different studies using nonlinear mixed-effects modelling. The models developed were used to estimate area under the concentration-time curve (AUC) and maximum concentrations (Cmax) achieved with the currently-recommended weight-adjusted doses. A web-based paediatric dosing tool, which is meant to be used as a first step in the design of clinical trials for paediatric dosing was also developed. The model describing the pharmacokinetics of abacavir found: a) abacavir exposure to be 18.4% larger (CI:7.50-32.2) after the first dose of ART compared to abacavir co-treated with standard lopinavir/ritonavir for over 7 days, possibly indicating that clearance is induced with time on ART, b) malnourished HIV infected children had much higher exposures but this effect waned with a half-life of 12.2 (CI: 9.87-16.8) days as children stayed on nutritional rehabilitation and recovered, c). during co-administration of rifampicin-containing antituberculosis treatment and super-boosted lopinavir/ritonavir, abacavir exposure was decreased by 29.4% (CI: 24.3-35.8), d) children receiving efavirenz had 12.1% (CI: 2.57-20.1) increased abacavir clearance compared to standard lopinavir/ritonavir. The effects did not result in abacavir exposures lower or higher than those reported in adults and are not likely to be clinically important. The ethambutol model found lower concentrations than those reported in adults. The predicted ethambutol median (IQR) Cmax was 1.66 (1.21-2.15) mg/L for children on ethambutol with or without ART (excluding super-boosted lopinavir/ritonavir) and 0.882 (0.669-1.28) mg/L for children on ethambutol with super-boosted lopinavir/ritonavir, these are below the lower limit of the recommended Cmax range of 2 mg/L. During co-administration with super-boosted lopinavir, ethambutol exposure was decreased by 32% (CI: 23.8-38.9), likely due to drug-drug interaction involving absorption, metabolism or elimination. Bioavailability was decreased in children who were administered ethambutol in a crushed form, with an estimate decrease of 30.8% at birth, and an increase of 9.6% for each year of age up to 3.2 years where bioavailability was now similar to children taking EMB full tablet. The developed paediatric dosing tool contains two major sections. a) the ‘generic module’ which uses allometric scaling -based predictions to explore the expected AUC for a generic drug, b) the ‘drug-specific module’ which can simulate entire pharmacokinetic profiles (concentration over time after dose) by using a drug-specific population pharmacokinetic model. In summary, under the current weight-adjusted doses, abacavir exposure remained within the adult recommended levels. Ethambutol dose adjustment would be required in order to achieve adult exposures. A web-based paediatric dosing tool that uses allometric scaling -based predictions as well as drug specific predictions based on published pharmacokinetic models was successfully developed

    Control-Oriented Model with Intra-Patient Variations for an Artificial Pancreas

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    In this work, a low-order model designed for glucose regulation in Type 1 Diabetes Mellitus (T1DM) is obtained from the UVA/Padova metabolic simulator. It captures not only the nonlinear behavior of the glucose-insulin system, but also intrapatient variations related to daily insulin sensitivity (SI) changes. To overcome the large inter-subject variability, the model can also be personalized based on a priori patient information. The structure is amenable for linear parameter varying (LPV) controller design, and represents the dynamics from the subcutaneous insulin input to the subcutaneous glucose output. The efficacy of this model is evaluated in comparison with a previous control-oriented model which in turn is an improvement of previous models. Both models are compared in terms of their open- and closed-loop differences with respect to the UVA/Padova model. The proposed model outperforms previous T1DM controloriented models, which could potentially lead to more robust and reliable controllers for glycemia regulation.Fil: Moscoso Vásquez, Hilda Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires. Departamento de Matemática. Centro de Sistemas y Control; ArgentinaFil: Colmegna, Patricio Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Virginia; Estados Unidos. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Rosales, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Garelli, Fabricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Sanchez Peña, Ricardo Salvador. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Tecnológico de Buenos Aires. Departamento de Matemática. Centro de Sistemas y Control; Argentin

    Anesthesiologist in the loop and predictive algorithm to maintain hypnosis while mimicking surgical disturbance

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    Many regulatory loops in drug delivery systems for depth of anesthesia optimization problem consider only the effect of the controller output on the patient pharmacokinetic and pharmacodynamic response. In reality, these drug assist devices are over-ruled by the anesthesiologist for setpoint changes, bolus intake and additional disturbances from the surgical team. Additionally, inter-patient variability imposes variations in the dynamic response and often intra-patient variability is also present. This paper introduces for the first time in literature a study on the effect of both controller and anesthesiologist in the loop for hypnosis regulation. Among the many control loops, model based predictive control is closest to mimic the anticipatory action of the anesthesiologist in real life and can actively deal with issues as time lags, delays, constraints, etc. This control algorithm is here combined with the action of the anesthesiologist. A disturbance signal to mimic surgical excitation has been introduced and a database of 25 patients has been derived from clinical insight. The results given in this paper reveal the antagonist effect in closed loop of the intervention from the anaesthesiologist when additional bolus intake is present. This observation explains induced dynamics in the closed loop observed in clinical trials and may be used as a starting point for next step in developing tools for improved assistance in clinical care. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

    Nonlinear model predictive control with logic constraints for COVID-19 management

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    The management of COVID-19 appears to be a long term chal- lenge, even in countries that have managed to suppress the epidemic after their initial outbreak. In this paper, we propose a model predictive approach for the constrained control of a nonlinear compartmental model that cap- tures the key dynamical properties of COVID-19. The control design uses the discrete-time version of the epidemic model, and it is able to handle complex, possibly time-dependent constraints, logical relations between model variables, and multiple predefined discrete levels of interventions. A state observer is also constructed for the computation of non-measured variables from the number of hospitalized patients. Five control scenarios with different cost functions and constraints are studied through numerical simulations, including an out- put feedback configuration with uncertain parameters. It is visible from the results that, depending on the cost function associated to different policy aims, the obtained controls correspond to mitigation and suppression strategies, and the constructed control inputs are similar to real life government responses. The results also clearly show the key importance of early intervention, the continuous tracking of the susceptible population and that of future work in determining the true costs of restrictive control measures and their quantitative effects
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