Simultaneous Nonlinear Model Predictive Control and State Estimation: Theory and Applications

As computational power increases, online optimization is becoming a ubiquitous approach for solving control and estimation problems in both academia and industry. This widespread popularity of online optimization techniques is largely due to their abilities to solve complex problems in real time and to explicitly accommodate hard constraints. In this dissertation, we discuss an especially popular online optimization control technique called model predictive control (MPC). Specifically, we present a novel output-feedback approach to nonlinear MPC, which combines the problems of state estimation and control into a single min-max optimization. In this way, the control and estimation problems are solved simultaneously providing an output-feedback controller that is robust to worst-case system disturbances and noise. This min-max optimization is subject to the nonlinear system dynamics as well as constraints that come from practical considerations such as actuator limits. Furthermore, we introduce a novel primal-dual interior-point method that can be used to efficiently solve the min-max optimization problem numerically and present several examples showing that the method succeeds even for severely nonlinear and non-convex problems. Unlike other output-feedback nonlinear optimal control approaches that solve the estimation and control problems separately, this combined estimation and control approach facilitates straightforward analysis of the resulting constrained, nonlinear, closed-loop system and yields improved performance over other standard approaches. Under appropriate assumptions that encode controllability and observability of the nonlinear process to be controlled, we show that this approach ensures that the state of the closed-loop system remains bounded. Finally, we investigate the use of this approach in several applications including the coordination of multiple unmanned aerial vehicles for vision-based target tracking of a moving ground vehicle and feedback control of an artificial pancreas system for the treatment of Type 1 Diabetes. We discuss why this novel combined control and estimation approach is especially beneficial for these applications and show promising simulation results for the eventual implementation of this approach in real-life scenarios

Contributions to modelling and control for improved hypoglycaemia and variability mitigation by dual-hormone artificial pancreas systems

[ES] Las personas con diabetes tipo 1 carecen de la capacidad de secretar insulina y, por lo tanto, necesitan regular su glucosa en sangre con la administración de insulina exógena. El páncreas artificial se presenta como la solución tecnológica ideal para alcanzar los objetivos terapéuticos de la normoglucemia, liberando al paciente de la carga actual de autocontrol y manejo. Sin embargo, el riesgo de hipoglucemia y la variabilidad glucémica siguen siendo factores limitantes en los algoritmos de control actuales integrados en el páncreas artificial. El propósito de la presente tesis es profundizar en el conocimiento de la hipoglucemia y avanzar los algoritmos de control del páncreas artificial para minimizar la incidencia de hipoglucemia y reducir la variabilidad glucémica. Después de proporcionar una visión general del estado del arte del control de la glucosa y el páncreas artificial, esta tesis aborda temas relacionados con el modelado y el control, con las siguientes contribuciones: Se presenta una extensión del modelo de Bergman Minimal que tiene en cuenta la respuesta contrarreguladora a la hipoglucemia. Este modelo explica la relación entre los diversos cambios fisiológicos producidos durante la hipoglucemia, con la adrenalina y los ácidos grasos libres como actores principales. Como resultado, se obtiene una mejor comprensión de la hipoglucemia, lo que permite explicar una auto-potenciación paradójica de la hipoglucemia como se modela a través de enfoques funcionales en el ampliamente utilizado simulador de diabetes tipo 1 UVA-Padova, que se utilizará en esta tesis para la validación in silico de los controladores desarrollados. Se realiza una evaluación de las métricas de variabilidad de la glucosa y los índices de calidad de control. La evaluación de la variabilidad glucémica en el desempeño de los controladores es necesaria; pero todavía no hay un conjunto de métricas de variabilidad glucémica que sea considerado como el "gold estándar". Por tanto, se lleva a cabo un análisis de las métricas de variabilidad disponibles en la literatura para definir un conjunto de indicadores recomendables. Debido a las limitaciones de los sistemas de páncreas artificiales unihormonales para mitigar la hipoglucemia en escenarios difíciles como el ejercicio, esta tesis se centra en el desarrollo de nuevos algoritmos de control bihormonales, con infusión simultanea de insulina y glucagón. Se propone un controlador coordinado bihormonal con estructuras de control paralelas como un algoritmo de control factible para la mitigación de la hipoglucemia y la reducción de la variabilidad glucémica, demostrando un rendimiento superior al de las estructuras de control utilizadas actualmente con lazos de control independientes de insulina y glucagón. Los controladores están diseñados y evaluados in silico en escenarios desafiantes y su rendimiento se evalúa principalmente con el conjunto de métricas definidas previamente como las recomendables.[CA] Les persones amb diabetis tipus 1 no tenen la capacitat de secretar insulina secreta i per tant, necessiten regular la seva glucosa en sang amb l'administració d'insulina exògena. El Pàncrees Artificial es presenta com la solució tecnològica ideal per assolir els objectius terapèutics de la normoglucèmia, alliberant al pacient de la càrrega actual d'autocontrol. No obstant, el risc d'hipoglucèmia i l'alta variabilitat glucèmica continuen sent un factor limitant en els algoritmes de control actuals integrats en el Pàncrees Artificials. El propòsit de la present tesi és aprofundir en el coneixement de la hipoglucèmia i millorar els algoritmes de control per corregir amb antelació la dosi excessiva d'insulina, minimitzant la incidència d'hipoglucèmia i reduint la variabilitat glucèmica. Després de donar una visió general de l'estat de l'art del control de la glucosa i el pàncrees artificial, aquesta tesi aborda aspectes de modelització i control, amb les següents contribucions: Es presenta una extensió del model Minimal de Bergman amb la contrarregulació. Aquest model explica la relació entre els diversos canvis siològics produïts durant la hipoglucèmia. Així, permet comprendre millor la hipoglucèmia i comparar els resultats amb els proporcionats per l'enfocament funcional del simulador de diabetis tipus 1 més utilitzat a la comunitat científica. Es realitza una avaluació de les mètriques de variabilitat glucèmica i dels índexs de qualitat de control. Es necessària l'avaluació de la variabilitat glucèmica en el rendiment dels controladors; però encara no hi ha un conjunt de mètriques considerades com les "gold standard". Per tant, es realitza una anàlisi de les mètriques de variabilitat disponibles a la literatura per definir un conjunt d'indicadors recomanables. Es proposa un controlador bi-hormonal coordinat amb estructures de control paral.leles com un algoritme de control viable per a la mitigació d'hipoglucèmia i la reducció de la variabilitat glucèmica. Els controladors estan dissenyats i avaluats in-silico en escenaris desafiadors i el seu rendiment es valora principalment amb el conjunt de mètriques definides prèviament com les mètriques recomanables.[EN] People with Type 1 Diabetes lack the ability to secrete insulin and therefore need to regulate their blood glucose with exogenous insulin delivery. The Artificial Pancreas is presented as the ideal technological solution to reach the therapeutic goals of normoglycaemia, freeing the patient from the current burden of self-control and management. Nevertheless, the risk of hypoglycaemia and the high glycaemic variability are still a limiting factors in the current control algorithms integrated in the Artificial Pancreas. The purpose of the present thesis is to delve into knowledge of hypoglycaemia and to advance in the artificial pancreas control algorithms in order to minimise hypoglycaemia incidence and reduce glycaemic variability. After providing an overview of the state of the art in the eld of glucose control and articial pancreas, this thesis addresses issues on modelling and control, with the following contributions: An extension of the Bergman Minimal model accounting for counterregulatory response to hypoglycaemia is presented. This model explains the relationship between the several physiological changes produced during hypoglycaemia, with adrenaline and free fatty acids as main players. As a result, a better understanding of hypoglycaemia is gained, allowing to explain a paradoxical auto-potentiation of hypoglycaemia as modeled through functional approaches in the widespread used UVA-Padova Type 1 Diabetes simulator, which will be used in this thesis for in silico validation of the developed controllers. An assessment of glucose variability metrics and control quality indices is carried out. The evaluation of the glycaemic variability on the controllers performance is necessary; but there is not a gold standard variability metrics yet. Therefore, an analysis of the variability metrics available in literature is conducted in order to define a recommendable set of indicators. Due to the limitations of single-hormone artificial pancreas systems in mitigating hypoglycaemia in challenging scenarios such as exercise, this thesis focuses on the developement of new dual-hormone control algorithms, with concomitant infusion of insulin and glucagon. A coordinated dual-hormone controller with parallel control structures is proposed as a feasible control algorithm for hypoglycaemia mitigation and glycaemic variability reduction, demonstrating superior performance as currently used control structures with independent insulin and glucagon control loops. The controllers are designed and evaluated in-silico under challenging scenarios and their performance are assessed mainly with the set of metrics defined previously as the recommendable ones.Moscardó García, V. (2019). Contributions to modelling and control for improved hypoglycaemia and variability mitigation by dual-hormone artificial pancreas systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/120456TESI

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 339)

This bibliography lists 105 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during July 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Non-Invasive Continuous Glucose Monitoring: Identification of Models for Multi-Sensor Systems

Diabetes is a disease that undermines the normal regulation of glucose levels in the blood. In people with diabetes, the body does not secrete insulin (Type 1 diabetes) or derangements occur in both insulin secretion and action (Type 2 diabetes). In spite of the therapy, which is mainly based on controlled regimens of insulin and drug administration, diet, and physical exercise, tuned according to self-monitoring of blood glucose (SMBG) levels 3-4 times a day, blood glucose concentration often exceeds the normal range thresholds of 70-180 mg/dL. While hyperglycaemia mostly affects long-term complications (such as neuropathy, retinopathy, cardiovascular, and heart diseases), hypoglycaemia can be very dangerous in the short-term and, in the worst-case scenario, may bring the patient into hypoglycaemic coma. New scenarios in diabetes treatment have been opened in the last 15 years, when continuous glucose monitoring (CGM) sensors, able to monitor glucose concentration continuously (i.e. with a reading every 1 to 5 min) over several days, entered clinical research. CGM sensors can be used both retrospectively, e.g., to optimize the metabolic control, and in real-time applications, e.g., in the "smart" CGM sensors, able to generate alerts when glucose concentrations are predicted to exceed the normal range thresholds or in the so-called "artificial pancreas". Most CGM sensors exploit needles and are thus invasive, although minimally. In order to improve patients comfort, Non-Invasive Continuous Glucose Monitoring (NI-CGM) technologies have been widely investigated in the last years and their ability to monitor glucose changes in the human body has been demonstrated under highly controlled (e.g. in-clinic) conditions. As soon as these conditions become less favourable (e.g. in daily-life use) several problems have been experienced that can be associated with physiological and environmental perturbations. To tackle this issue, the multisensor concept received greater attention in the last few years. A multisensor consists in the embedding of sensors of different nature within the same device, allowing the measurement of endogenous (glucose, skin perfusion, sweating, movement, etc.) as well as exogenous (temperature, humidity, etc.) factors. The main glucose related signals and those measuring specific detrimental processes have to be combined through a suitable mathematical model with the final goal of estimating glucose non-invasively. White-box models, where differential equations are used to describe the internal behavior of the system, can be rarely considered to combine multisensor measurements because a physical/mechanistic model linking multisensor data to glucose is not easily available. A more viable approach considers black-box models, which do not describe the internal mechanisms of the system under study, but rather depict how the inputs (channels from the non-invasive device) determine the output (estimated glucose values) through a transfer function (which we restrict to the class of multivariate linear models). Unfortunately, numerical problems usually arise in the identication of model parameters, since the multisensor channels are highly correlated (especially for spectroscopy based devices) and for the potentially high dimension of the measurement space. The aim of the thesis is to investigate and evaluate different techniques usable for the identication of the multivariate linear regression models parameters linking multisensor data and glucose. In particular, the following methods are considered: Ordinary Least Squares (OLS); Partial Least Squares (PLS); the Least Absolute Shrinkage and Selection Operator (LASSO) based on l1 norm regularization; Ridge regression based on l2 norm regularization; Elastic Net (EN), based on the combination of the two previous norms. As a case study, we consider data from the Multisensor device mainly based on dielectric and optical sensors developed by Solianis Monitoring AG (Zurich, Switzerland) which partially sponsored the PhD scholarship. Solianis Monitoring AG IP portfolio is now held by Biovotion AG (Zurich, Switzerland). Forty-five recording sessions provided by Solianis Monitoring AG and collected in 6 diabetic human beings undertaken hypo and hyperglycaemic protocols performed at the University Hospital Zurich are considered. The models identified with the aforementioned techniques using a data subset are then assessed against an independent test data subset. Results show that methods controlling complexity outperform OLS during model test. In general, regularization techniques outperform PLS, especially those embedding the l1 norm (LASSO end EN), because they set many channel weights to zero thus resulting more robust to occasional spikes occurring in the Multisensor channels. In particular, the EN model results the best one, sharing both the properties of sparseness and the grouping effect induced by the l1 and l2 norms respectively. In general, results indicate that, although the performance, in terms of overall accuracy, is not yet comparable with that of SMBG enzyme-based needle sensors, the Multisensor platform combined with the Elastic-Net (EN) models is a valid tool for the real-time monitoring of glycaemic trends. An effective application concerns the complement of sparse SMBG measures with glucose trend information within the recently developed concept of dynamic risk for the correct judgment of dangerous events such as hypoglycaemia. The body of the thesis is organized into three main parts: Part I (including Chapters 1 to 4), first gives an introduction of the diabetes disease and of the current technologies for NI-CGM (including the Multisensor device by Solianis) and then states the aims of the thesis; Part II (which includes Chapters 5 to 9), first describes some of the issues to be faced in high dimensional regression problems, and then presents OLS, PLS, LASSO, Ridge and EN using a tutorial example to highlight their advantages and drawbacks; Finally, Part III (including Chapters 10-12), presents the case study with the data set and results. Some concluding remarks and possible future developments end the thesis. In particular, a Monte Carlo procedure to evaluate robustness of the calibration procedure for the Solianis Multisensor device is proposed, together with a new cost function to be used for identifying models

Aerospace Medicine and Biology: A cumulative index to the 1974 issues of a continuing bibliography

This publication is a cumulative index to the abstracts contained in supplements 125 through 136 of Aerospace Medicine and Biology: A Continuing Bibliography. It includes three indexes--subject, personal author, and corporate source

Aerospace medicine and biology: A cumulative index to a continuing bibliography (supplement 345)

This publication is a cumulative index to the abstracts contained in Supplements 333 through 344 of Aerospace Medicine and Biology: A Continuing Bibliography. Seven indexes are included -- subject, personal author, corporate source, foreign technology, contract number, report number, and accession number