Control Automático

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The ARG algorithm: clinical trials in Argentina
(2019) Colmegna, Patricio; Garelli, Fabricio; De Battista, Hernán; Bianchi, Fernando D.; Sánchez-Peña, Ricardo
"The objective of this work is to present a brief review of the control design problem for glucose regulation in T1DM. In particular, control-oriented models, and robust and time-varying controllers will be mentioned. Characteristics of diabetes in general and T1DM in particular in the context of Latin America will be described. Finally, the Automatic Regulation of Glucose (ARG) algorithm will be presented, including in silico and clinical results."
Artificial pancreas: clinical study in Latin America without premeal insulin boluses
(2018-09) Sánchez-Peña, Ricardo; Colmegna, Patricio; Garelli, Fabricio; De Battista, Hernán; García Violini, Demián; Moscoso-Vásquez, Marcela; Rosales, Nicolás; Fushimi, Emilia; Campos-Náñez, Enrique; Breton, Marc; Beruto, Valeria; Scibona, Paula; Rodriguez, Cintia; Giunta, Javier; Simonovich, Ventura; Belloso, Waldo H.; Cherñavvsky, Daniel; Grosembacher, Luis
"Background: Emerging therapies such as closed-loop (CL) glucose control, also known as artificial pancreas (AP) systems, have shown significant improvement in type 1 diabetes mellitus (T1DM) management. However, demanding patient intervention is still required, particularly at meal times. To reduce treatment burden, the automatic regulation of glucose (ARG) algorithm mitigates postprandial glucose excursions without feedforward insulin boluses. This work assesses feasibility of this new strategy in a clinical trial. Methods: A 36-hour pilot study was performed on five T1DM subjects to validate the ARG algorithm. Subjects wore a subcutaneous continuous glucose monitor (CGM) and an insulin pump. Insulin delivery was solely commanded by the ARG algorithm, without premeal insulin boluses. This was the first clinical trial in Latin America to validate an AP controller. Results: For the total 36-hour period, results were as follows: average time of CGM readings in range 70-250 mg/dl: 88.6%, in range 70-180 mg/dl: 74.7%, <70 mg/dl: 5.8%, and <50 mg/dl: 0.8%. Results improved analyzing the final 15-hour period of this trial. In that case, the time spent in range was 70-250 mg/dl: 94.7%, in range 70-180 mg/dl: 82.6%, <70 mg/dl: 4.1%, and <50 mg/dl: 0.2%. During the last night the time spent in range was 70-250 mg/dl: 95%, in range 70-180 mg/dl: 87.7%, <70 mg/dl: 5.0%, and <50 mg/dl: 0.0%. No severe hypoglycemia occurred. No serious adverse events were reported. Conclusions: The ARG algorithm was successfully validated in a pilot clinical trial, encouraging further tests with a larger number of patients and in outpatient settings."
Artificial Pancreas: first clinical trials in Argentina
(2017-07) Sánchez-Peña, Ricardo; Colmegna, Patricio; Grosembacher, Luis; Breton, Marc; De Battista, Hernán; Garelli, Fabricio; Belloso, Waldo H.; Campos-Náñez, Enrique; Simonovich, Ventura; Beruto, Valeria; Scibona, Paula; Cherñavvsky, Daniel
"The first clinical trials using an Artificial Pancreas (AP) in Latin America have been defined in 2 stages. The first stage was carried out in November 2016 with the UVA controller (developed by the Center for Diabetes Technology and already clinically tested), and the second will be performed during the first semester of 2017 with the ARG (Automatic Regulation of Glucose) algorithm (developed by ITBA, UNQ, and UNLP in Argentina). Both tests are based on the DiAs (Diabetes Assistant) from the UVA, and are performed in the HIBA on 5 patients with Type 1 Diabetes Mellitus (T1DM), for 36 hours. For the first stage, Open-Loop (OL) insulin boluses were applied before meals and patient's physical activity was included. On the other hand, for the second stage, patients will not be involved in physical activity, but no OL insulin boluses will be injected before meals. In this work, experimental results from the first stage with the UVA controller, and preliminary results with the ARG control algorithm tested on the UVA/Padova simulator are presented. Due to the final paper deadline, the experimental results from the second stage are not included here, but will be presented at the IFAC World Congress."
Automatic glucose control during meals and exercise in type 1 diabetes: proof-of-concept in silico tests using a switched LPV approach
(2021) Colmegna, Patricio; Bianchi, Fernando D.; Sánchez-Peña, Ricardo
"Keeping the blood glucose levels within the safe range during meals and exercise still represents a major hurdle not only for patients with type 1 diabetes (T1D), but also for Artificial Pancreas (AP) systems. One of the reasons a fully (autonomous) closed-loop solution has not been released onto the market yet is the slow action of current insulin analogs. To partially overcome this limitation, the authors have previously designed a switched control strategy equipped with an insulin-on-board (IOB) safety loop that mitigates meal-related glucose excursions without carbohydrate counting. In this paper, a similar strategy based on a Linear Parameter-Varying (LPV) control law has been adapted to safely handle also exercise challenges with minimum user intervention. In silico results using the UVA/Padova simulator evidence that the proposed closedloop scheme is feasible under moderate-intense exercise bouts by effectively and safely reducing the risk of hypoglycemia."
Automatic regulatory control in type 1 diabetes without carbohydrate counting
(2018-05) Colmegna, Patricio; Garelli, Fabricio; De Battista, Hernán; Sánchez-Peña, Ricardo
"A new approach to automatically regulate the glucose level in type 1 diabetes is presented in this work. This is the so-called Automatic Regulation of Glucose (ARG) algorithm, which is based on a switched Linear Quadratic Gaussian (LQG) inner controller, combined with an outer sliding mode safety loop with Insulin on Board (IOB) constraints. In silico and in vivo results without feedforward insulin boluses delivered at meal times indicate that safe blood glucose control can be achieved by the proposed controller. This controller is simple to migrate to well-known hardware platforms, and intuitive to tune using a priori clinical information."
Control-oriented linear parameter-varying model for glucose control in type 1 diabetes
(2016) Colmegna, Patricio; Sánchez-Peña, Ricardo; Gondhalekar, Ravi
"The contribution of this paper is a controller design oriented model of insulin-glucose dynamics in Type 1 Diabetes Mellitus (T1DM). The novelty of the proposed model is to more effectively include the time-varying nature, and also the inter-patient variability, associated with the glucose control problem. Importantly, this is achieved in a manner that straightforwardly facilitates well-known and standard controller synthesis procedures. In that way, an average Linear Parameter-Varying (LPV) model that captures the dynamics from the insulin delivery input to the subcutaneous-glucose concentration output is constructed based on the Universities of Virginia (UVA)/Padova metabolic simulator. In addition, a system-oriented reinterpretation of the classical ad-hoc 1800 rule is applied to adapt the model’s gain."
Control-oriented model with intra-patient variations for an artificial pancreas
(2020) Moscoso-Vásquez, Marcela; Colmegna, Patricio; Rosales, Nicolás; Garelli, Fabricio; Sánchez-Peña, Ricardo
"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 eficacy 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 control oriented models, which could potentially lead to more robust and reliable controllers for glycemia regulation."
Invalidation and low-order model set for artificial pancreas robust control design
(2019) Bianchi, Fernando D.; Moscoso-Vásquez, Marcela; Colmegna, Patricio; Sánchez-Peña, Ricardo
"The purpose of this work is to compute a linear parameter-varying (LPV) model set that describes the insulin-glucose dynamics in type 1 diabetes (T1D). This set includes a nominal LPV model and dynamic uncertainty and is amenable to controller design. The nominal model is an LPV control-oriented model previously published by the authors that is (in)validated in this work against the UVA/Padova metabolic simulator. The result is a set of models that is used to design a switched LPV robust controller to account for nonlinearities and variations in insulin sensitivity (SI). Closed-loop responses obtained with the robust controller and a nominal one are compared. Results illustrate the convenience of including robust strategies in designing control laws for an artificial pancreas (AP). "
Linear parameter-varying model to design control laws for an artificial pancreas
(2018-02) Colmegna, Patricio; Sánchez-Peña, Ricardo; Gondhalekar, Ravi
"The contribution of this work is the generation of a control-oriented model for insulin-glucose dynamic regulation in type1 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."
Reducing glucose variability due to meals and postprandial exercise in TI DM using switched LPV control: in silico studies
(2016) Colmegna, Patricio; Sánchez-Peña, Ricardo; Gondhalekar, Ravi; Dassau, Eyal; Doyle III, Frank J.
"Time-varying dynamics is one of the main issues for achieving safe blood glucose control in type I diabetes mellitus (TI DM) patients. In addition, the typical disturbances considered for controller design are meals, which increase the glucose level, and physical activity (PA), which increases the subject's sensitivity to insulin. In previous works the authors have applied a linear parameter-varying (LPV) control technique to manage unannounced meals."
Switched LPV glucose control in type 1 diabetes
(2016-06) Colmegna, Patricio; Sánchez-Peña, Ricardo; Gondhalekar, Ravi; Dassau, Eyal; Doyle III, Frank J.
"The purpose of this work is to regulate the blood glucose level in Type 1 Diabetes Mellitus (T1DM) patients with a practical and flexible procedure that can switch amongst a finite number of distinct controllers, depending on the user´s choice."
Unannounced meal analysis of the ARG algorithm
(2019-07) Fushimi, Emilia; Colmegna, Patricio; De Battista, Hernán; Garelli, Fabricio; Sánchez-Peña, Ricardo
"One of the main challenges in automatic glycemic regulation in patients with type 1 diabetes (T1D) is to dispense with carbohydrate counting. In this context, we propose to equip a previously introduced switched Linear Quadratic Gaus-sian (LQG) controller—the so-called Automatic Regulation of Glucose (ARG) algorithm—with an automatic switching signal generator (SSG). The ARG algorithm not only regulates the basal insulin infusion rate but also generates feedback insulin spikes at meal times, i.e., no open-loop insulin boluses are needed to mitigate postprandial glucose excursions. However, in its former version, it was required to announce the meal time. In this work, the performance of the ARG algorithm combined with the proposed SSG is assessed in silico with unannounced meals. In addition, the response of the SSG is estimated using clinical data obtained with the ARG algorithm in the first-ever artificial pancreas (AP) trials carried out in Latin America."

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