Complex-order PID controller design for enhanced blood-glucose regulation in Type-I diabetes patients

Type-I Diabetes (TID) is a chronic autoimmune disease that elevates the glucose levels in the patient’s bloodstream. This paper formulates a fractional complex-order Proportional-Integral-Derivative (PID) control strategy for robust Blood Glucose (BG) regulation in TID patients. The glucose-insulin dynamics in blood plasma are modeled via the Bergman-Minimal-Model. The proposed control procedure employs the ubiquitous fractional order PID controller as the baseline BG regulator. The design flexibility of the baseline regulator to effectively normalize the BG levels is enhanced by assigning complex orders to the integral and differential operators instead. The resulting Complex Order PID (CO-PID) regulator strengthens the controller’s robustness against abrupt variations in the patient’s BG levels caused by meal disturbances or sensor noise. The controller parameters are numerically optimized offline. The aforesaid propositions are justified by performing credible simulations in which the proposed controller is tasked to effectively track a set point value of 80 mg/dL from an initial state of hyperglycemia under various disturbance factors. As compared to the FO-PID controller, the CO-PID controller improves the reference tracking-error, transient recovery-time, and control expenditure by 13.1%, 33.4%, and 28.1%, respectively. The simulation results validate the superior reference-tracking accuracy of the proposed CO-PID controller for BG regulation

Artificial Pancreas: In Silico Study Shows No Need of Meal Announcement and Improved Time in Range of Glucose with Intraperitoneal vs. Subcutaneous Insulin Delivery

Contemporary Artificial Pancreas (AP) consists of a subcutaneous (SC) glucose sensor, a SC insulin pump and a control algorithm. Even the most advanced systems are far from optimal, in particular due to the non-physiologic nature of SC route. While SC insulin delivery is convenient and minimally invasive, it introduces delays to insulin action that make tight control difficult, particularly during meals. In addition frequent patient interventions are needed, e.g., at mealtime. The intraperitoneal (IP) insulin delivery could address this major challenge since it exhibits a faster pharmacokinetics/pharmacodynamics, hence making easier to quickly respond to glycemic disturbances. A 1-day hospital closed-loop study has shown significant improvements of IP glucose control vs SC AP, and that meal announcement is not necessary. However, the IP AP has not been tested in more realistic everyday life conditions. In this work we have performed an in silico study of 14 days of an IP AP by using the UVA/Padova simulator which includes intra- and inter-day variability of insulin sensitivity and several real life scenarios. We show superiority of IP AP vs SC AP in terms of quality of glucose control (time in range 87% IP vs 80% SC) without the need of a meal announcement

Predictive Control of Diabetic Glycemia

Diabetes Mellitus is a chronic disease, where the blood glucose concentration of the patient is elevated. This is either because of missing insulin production due to failure of the β-cells in the pancreas (Type 1) or because of reduced sensitivity of the cells in the body to insulin (Type 2). The therapy for Type 1 diabetic patients usually consists of insulin injections to substitute for the missing insulin. The decision about the amount of insulin to be taken has to be made by the patient, based on empirically developed rules of thumb. To help the patient with this task, advanced mathematical algorithms were used in this thesis to determine intakes of insulin and counteracting glucose that can bring the blood glucose concentration back to normoglycemia. The focus in this work was to determine insulin and glucose intakes around mealtimes. These algorithms used optimization methods together with predictions of the blood glucose concentration and mathematical models describing the patient dynamics to determine the insulin and glucose doses. For evaluation, the control algorithms were tested insilico using a virtual patient and are compared to a simple bolus calculator from the literature. The aim was to increase the time spent in the safe range of blood glucose values of 70 − 180 [mg/dL]

Diabetes Monitoring System

This is the author accepted manuscript. The final version is available from CRC Press via the DOI in this recordThis chapter reviews the current technologies for monitoring and intervention of diabetes. Especially various blood glucose concentration estimation, online signal monitoring and adaptive control mechanisms are discussed. Recent research has proposed many control engineering approaches for Type 1 diabetes and many algorithms of artificial pancreas have been proposed. This book chapter reviews the current state of the art and industrial standards on diabetes monitoring and control

On adaptive control and particle filtering in the automatic administration of medicinal drugs

Automatic feedback methodologies for the administration of medicinal drugs offer undisputed potential benefits in terms of cost reduction and improved clinical outcomes. However, despite several decades of research, the ultimate safety of many--it would be fair to say most--closed-loop drug delivery approaches remains under question and manual methods based on clinicians' expertise are still dominant in clinical practice. Key challenges to the design of control systems for these applications include uncertainty in pharmacological models, as well as intra- and interpatient variability in the response to drug administration. Pharmacological systems may feature nonlinearities, time delays, time-varying parameters and non-Gaussian stochastic processes. This dissertation investigates a novel multi-controller adaptive control strategy capable of delivering safe control for closed-loop drug delivery applications without impairing clinicians' ability to make an expert assessment of a clinical situation. Our new feedback control approach, which we have named Robust Adaptive Control with Particle Filtering (RAC-PF), estimates a patient's individual response characteristic in real-time through particle filtering and uses the Bayesian inference result to select the most suitable controller for closed-loop operation from a bank of candidate controllers designed using the robust methodology of mu-synthesis. The work is presented as four distinct pieces of research. We first apply the existing approach of Robust Multiple-Model Adaptive Control (RMMAC), which features robust controllers and Kalman filter estimators, to the case-study of administration of the vasodepressor drug sodium nitroprusside and examine benefits and drawbacks. We then consider particle filtering as an alternative to Kalman filter-based methods for the real-time estimation of pharmacological dose-response, and apply this to the nonlinear pharmacokinetic-pharmacodynamic model of the anaesthetic drug propofol. We ultimately combine particle filters and robust controllers to create RAC-PF, and test our novel approach first in a proof-of-concept design and finally in the case of sodium nitroprusside. The results presented in the dissertation are based on computational studies, including extensive Monte-Carlo simulation campaigns. Our findings of improved parameter estimates from noisy observations support the use of particle filtering as a viable tool for real-time Bayesian inference in pharmacological system identification. The potential of the RAC-PF approach as an extension of RMMAC for closed-loop control of a broader class of systems is also clearly highlighted, with the proposed new approach delivering safe control of acute hypertension through sodium nitroprusside infusion when applied to a very general population response model. All approaches presented are generalisable and may be readily adapted to other drug delivery instances

An adaptive real-time intelligent system to enhance self-care of chronic disease (ARISES)

Diabetes mellitus is an increasingly prevalent chronic metabolic condition characterised by impaired glucose homeostasis and raised blood glucose levels (hyperglycaemia). Broadly categorised as either type 1 (T1DM) or type 2 diabetes (T2DM), people with diabetes are largely responsible for self-managing their blood glucose levels. Despite the development of diabetes technologies such as real time continuous glucose monitoring (RT-CGM), many individuals are frequently exposed to iatrogenic low blood glucose levels (hypoglycaemia). Severe hypoglycaemia is associated with an increased risk of recurrent hypoglycaemia, impaired symptomatic awareness of hypoglycaemia, and potentially death if left untreated. This thesis affirmed the existing clinical impact of severe hypoglycaemia and its recurrent risk in a six-month analysis of severe hypoglycaemia attended by the London Ambulance Service NHS Trust (LAS). Fewer incidents of severe hypoglycaemia observed in a date matched repeat analysis during the 2020 COVID-19 lockdown suggested improved self-management possibly motivated by a proximal fear of hospitalisation and improved structure at home. Finally, a 12-week randomised control trial demonstrating a significant difference in time spent in hypoglycaemia <3mmol/L, is the first study to prove the immediate provision of RT-CGM significantly reduces the risk of recurrent hypoglycaemia. Moreover, it highlighted the impact of socioeconomic disparity as a barrier to effective hypoglycaemia risk modification. This guided the design of an adaptive real time intelligent system to enhance self-care of chronic disease (ARISES) aimed to deliver therapeutic and lifestyle decision support for people with T1DM. The ARISES graphic user interface (GUI) design was a collaborative process conceived in a series of focus group meetings including people with T1DM. Finally, a 12-week observational study using RT-CGM, a physiological sensor wristband, and a mobile diary app, allowed for a sub-analysis identifying measurable physiological parameters associated with current and impending hypoglycaemia in people with T1DM.Open Acces

Modelling, optimisation and model predictive control of insulin delivery systems in Type 1 Diabetes Mellitus

Type 1 Diabetes Mellitus is a metabolic disease requiring lifelong treatment with exogenous insulin which significantly affects patient’s lifestyle. Therefore, it is of paramount importance to develop novel drug delivery techniques that achieve therapeutic efficacy and ensure patient safety with a minimum impact on their quality of life. Motivated by the challenge to improve the living standard of a diabetic patient, the idea of an artificial pancreas that mimics the endocrine function of a healthy pancreas has been developed in the scientific society. Towards this direction, model predictive control has been established as a very promising control strategy for blood glucose regulation in a system that is dominated by high intra- and inter-patient variability, long time delays, and presence of unknown disturbances such as diet, physical activity and stress levels. This thesis presents a framework for blood glucose regulation with optimal insulin infusion which consists of the following steps: 1. Development of a novel physiologically based compartmental model analysed up to organ level that describes glucose-insulin interactions in type 1 diabetes, 2. Derivation of an approximate model suitable for control applications, 3. Design of an appropriate control strategy and 4. In-silico validation of the closed loop control performance. The developed model’s accuracy and prediction ability is evaluated with data obtained from the literature and the UVa/Padova Simulator model, the model parameters are individually estimated and their effect on the model’s measured output, the blood glucose concentration, is identified. The model is then linearised and reduced to derive low-order linear approximations of the underlying system suitable for control applications. The proposed control design aims towards an individualised optimal insulin delivery that consists of a patient-specific model predictive controller, a state estimator, a personalised scheduling level and an open loop optimisation problem subjected to patient specific process model and constraints. This control design is modifiable to address the case of limited patient data availability resulting in an “approximation” control strategy. Both designs are validated in-silico in the presence of predefined, measured and unknown meal disturbances using both the proposed model and the UVa/Padova Simulator model as a virtual patient. The robustness of the control performance is evaluated in several conditions such as skipped meals, variability in the meal content, time and metabolic uncertainty. The simulation results of the closed loop validation studies indicate that the proposed control strategies can achieve promising glycaemic control as demonstrated by the study data. However, further prospective validation of the closed loop control strategy with real patient data is required.Open Acces