Arbitrary‐order sliding mode‐based robust control algorithm for the developing artificial pancreas mechanism

In Diabetes Mellitus, the pancreas remains incapable of insulin administration that leads to hyperglycaemia, an escalated glycaemic concentration, which may stimulate many complications. To circumvent this situation, a closed‐loop control strategy is much needed for the exogenous insulin infusion in diabetic patients. This closed‐loop structure is often termed as an artificial pancreas that is generally established by the employment of different feedback control strategies. In this work, the authors have proposed an arbitrary‐order sliding mode control approach for development of the said mechanism. The term, arbitrary, is exercised in the sense of its applicability to any n ‐order controllable canonical system. The proposed control algorithm affirms the finite‐time effective stabilisation of the glucose–insulin regulatory system, at the desired level, with the alleviation of sharp fluctuations. The novelty of this work lies in the sliding manifold that incorporates indirect non‐linear terms. In addition, the necessary discontinuous terms are filtered‐out once before its employment to the plant, i.e. diabetic patient. The robustness, in the presence of external disturbances, i.e. meal intake is confirmed via rigorous mathematical stability analysis. In addition, the effectiveness of the proposed control strategy is ascertained by comparing the results with the standard literature.Inspec keywords: diseases, blood, control system synthesis, medical control systems, feedback, sugar, stability, closed loop systems, robust control, variable structure systemsOther keywords: finite‐time effective stabilisation, glucose–insulin regulatory system, sliding manifold, nonlinear terms, necessary discontinuous terms, employment, diabetic patient, arbitrary‐order sliding mode‐based robust control algorithm, developing artificial pancreas mechanism, Diabetes Mellitus, insulin administration, escalated glycaemic concentration, closed‐loop control strategy, exogenous insulin infusion, closed‐loop structure, different feedback control strategies, mode control approach, n‐order controllable canonical system, control algorithm affirms     

Robust observer based control for plasma glucose regulation in type 1 diabetes patient using attractive ellipsoid method

This paper deals with the design of robust observer based output feedback control law for the stabilisation of an uncertain nonlinear system and subsequently apply the developed method for the regulation of plasma glucose concentration in Type 1 diabetes (T1D) patients. The principal objective behind the proposed design is to deal with the issues of intra‐patient parametric variation and non‐availability of all state variables for measurement. The proposed control technique for the T1D patient model is based on the attractive ellipsoid method (AEM). The observer and controller conditions are obtained in terms of linear matrix inequality (LMI), thus allowing to compute easily both the observer and controller gains. The closed‐loop response obtained using the designed controller avoids adverse situations of hypoglycemia and post‐prandial hyperglycemia under uncertain conditions. Further to validate the robustness of the design, closed‐loop simulations of random 200 virtual T1D patients considering parameters within the considered ranges are presented. The results indicate that hypoglycemia and post‐prandial hyperglycemia are significantly reduced in the presence of bounded (±30% ) parametric variability and uncertain exogenous meal disturbance.Inspec keywords: medical control systems, observers, uncertain systems, nonlinear control systems, robust control, control system synthesis, linear matrix inequalities, feedback, sugar, closed loop systems, diseasesOther keywords: virtual T1D patients, type 1 diabetes patients, closed‐loop simulations, uncertain conditions, post‐prandial hyperglycemia, designed controller, closed‐loop response, controller gains, linear matrix inequality, controller conditions, T1D patient model, control technique, intra‐patient parametric variation, principal objective, plasma glucose concentration, uncertain nonlinear system, robust observer based output feedback control law, attractive ellipsoid method, plasma glucose regulation     

Robust multi‐objective blood glucose control in Type‐1 diabetic patient

In this study, an automatic robust multi‐objective controller has been proposed for blood glucose (BG) regulation in Type‐1 Diabetic Mellitus (T1DM) patient through subcutaneous route. The main objective of this work is to control the BG level in T1DM patient in the presence of unannounced meal disturbances and other external noises with a minimum amount of insulin infusion rate. The multi‐objective output‐feedback controller with H_∞, H₂ and pole‐placement constraints has been designed using linear matrix inequality technique. The designed controller for subcutaneous insulin delivery was tested on in silico adult and adolescent subjects of UVa/Padova T1DM metabolic simulator. The experimental results show that the closed‐loop system tracks the reference BG level very well and does not show any hypoglycaemia effect even during the long gap of a meal at night both for in silico adults and adolescent. In the presence of 50 gm meal disturbance, average adult experience normoglycaemia 92% of the total simulation time and hypoglycaemia 0% of total simulation time. The robustness of the controller has been tested in the presence of irregular meals and insulin pump noise and error. The controller yielded robust performance with a lesser amount of insulin infusion rate than the other designed controllers reported earlier.Inspec keywords: robust control, patient treatment, diseases, closed loop systems, patient monitoring, biochemistry, medical control systems, blood, organic compoundsOther keywords: robust multiobjective blood glucose control, automatic robust multiobjective controller, blood glucose regulation, Type‐1 Diabetic Mellitus patient, BG level, T1DM patient, insulin infusion rate, multiobjective output‐feedback controller, pole‐placement constraints, linear matrix inequality technique, subcutaneous insulin delivery, total simulation time, insulin pump noise, adolescent subjects, meal disturbance, normoglycaemia 92, in silico adults, UVa‐Padova T1DM metabolic simulator, closed‐loop system, hypoglycaemia effect     

Blood glucose regulation in type 1 diabetic patients: an adaptive parametric compensation control‐based approach

Here, a direct adaptive control strategy with parametric compensation is adopted for an uncertain non‐linear model representing blood glucose regulation in type 1 diabetes mellitus patients. The uncertain parameters of the model are updated by appropriate design of adaptation laws using the Lyapunov method. The closed‐loop response of the plasma glucose concentration as well as external insulin infusion rate is analysed for a wide range of variation of the model parameters through extensive simulation studies. The result indicates that the proposed adaptive control scheme avoids severe hypoglycaemia and gives satisfactory performance under parametric uncertainty highlighting its ability to address the issue of inter‐patient variability.Inspec keywords: patient monitoring, adaptive control, diseases, Lyapunov methods, closed loop systems, medical control systems, patient treatment, medical computing, sugar, uncertain systems, blood, nonlinear control systems, physiological modelsOther keywords: blood glucose regulation, type 1 diabetic patients, adaptive parametric compensation control‐based approach, direct adaptive control strategy, nonlinear model, type 1 diabetes mellitus patients, uncertain parameters, appropriate design, adaptation laws, closed‐loop response, plasma glucose concentration, external insulin infusion rate, model parameters, adaptive control scheme, parametric uncertainty, inter‐patient variability     

Chattering‐ free hybrid adaptive neuro‐fuzzy inference system‐particle swarm optimisation data fusion‐based BG‐level control

In this study, a closed‐loop control scheme is proposed for the glucose–insulin regulatory system in type‐1 diabetic mellitus (T1DM) patients. Some innovative hybrid glucose–insulin regulators have combined artificial intelligence such as fuzzy logic and genetic algorithm with well known Palumbo model to regulate the blood glucose (BG) level in T1DM patients. However, most of these approaches have focused on the glucose reference tracking, and the qualitative of this tracking such as chattering reduction of insulin injection has not been well‐studied. Higher‐order sliding mode (HoSM) controllers have been employed to attenuate the effect of chattering. Owing to the delayed nature and non‐linear property of glucose–insulin mechanism as well as various unmeasurable disturbances, even the HoSM methods are partly successful. In this study, data fusion of adaptive neuro‐fuzzy inference systems optimised by particle swarm optimisation has been presented. The excellent performance of the proposed hybrid controller, i.e. desired BG‐level tracking and chattering reduction in the presence of daily glucose‐level disturbances is verified.Inspec keywords: fuzzy control, variable structure systems, particle swarm optimisation, neurocontrollers, fuzzy neural nets, blood, genetic algorithms, closed loop systems, medical control systems, fuzzy reasoning, diseases, nonlinear control systems, sugarOther keywords: data fusion, adaptive neuro‐fuzzy inference systems, particle swarm optimisation, hybrid controller, desired BG‐level tracking, chattering reduction, daily glucose‐level disturbances, closed‐loop control scheme, glucose–insulin regulatory system, type‐1 diabetic mellitus patients, innovative hybrid glucose–insulin regulators, artificial intelligence, fuzzy logic, genetic algorithm, Palumbo model, blood glucose level, T1DM patients, glucose reference tracking, insulin injection, mode controllers, glucose–insulin mechanism, chattering‐free hybrid adaptive neuro‐fuzzy inference system, particle swarm optimisation data fusion‐based BG‐level control     

Novel algebraic meal disturbance estimation based adaptive robust control design for blood glucose regulation in type 1 diabetes patients

This study designs a robust closed‐loop control algorithm for elevated blood glucose level stabilisation in type 1 diabetic patients. The control algorithm is based on a novel control action resulting from integrating algebraic meal disturbance estimator with back‐stepping integral sliding mode control (BISMC) technique. The estimator shows finite time convergence leading to accurate and fast estimation of meal disturbance. Moreover, compensation of the estimated disturbance in controller provides significant reduction in chattering phenomenon, which is inherent drawback of sliding mode control (SMC). The controller is applied to one of the most reliable models of type 1 diabetic patients, named Bergman''s minimal model. The effectiveness and superiority of the designed controller is shown by comparing it to classical SMC and super‐twisting sliding mode control. The designed controller is subject to three different cases for detailed analysis of the controller''s robustness against meal disturbance. The three cases considered are hyperglycaemia, hyperglycaemia combined with meal disturbance and three meal disturbance. The simulation results confirm superior performance of algebraic disturbance estimator based BISMC controller for all the cases mentioned above.Inspec keywords: closed loop systems, robust control, sugar, medical control systems, variable structure systems, control system synthesis, blood, nonlinear control systems, adaptive control, diseasesOther keywords: adaptive robust control design, blood glucose regulation, type 1 diabetes patients, closed‐loop control algorithm, elevated blood glucose level stabilisation, type 1 diabetic patients, novel control action, algebraic meal disturbance estimator, mode control technique, accurate estimation, estimated disturbance, super‐twisting sliding mode control, algebraic disturbance estimator, BISMC controller, algebraic meal disturbance estimation, back‐stepping integral sliding mode control technique     

Blood glucose concentration control for type 1 diabetic patients: a multiple‐model strategy

In this study, a multiple‐model strategy is evaluated as an alternative closed‐loop method for subcutaneous insulin delivery in type 1 diabetes. Non‐linearities of the glucose–insulin regulatory system are considered by modelling the system around five different operating points. After conducting some identification experiments in the UVA/Padova metabolic simulator (accepted simulator by the US Food and Drug Administration (FDA)), five transfer functions are obtained for these operating points. Paying attention to some physiological facts, the control objectives such as the required settling time and permissible bounds of overshoots and undershoots are determined for any transfer functions. Then, five PID controllers are tuned to achieve these objectives and a bank of controllers is constructed. To cope with difficulties of the presence of delays in subcutaneous blood glucose (BG) measuring and in administration of insulin, a glucose‐dependent setpoint is considered as the desired trajectory for the BG concentration. The performance of the obtained closed‐loop glucose–insulin regulatory system is investigated on the in silico adult cohort of the UVA/Padova metabolic simulator. The obtained results show that the proposed multiple‐model strategy leads to a closed‐loop mechanism with limited hyperglycemia and no severe hypoglycemia.Inspec keywords: blood, patient diagnosis, medical control systems, biochemistry, three‐term control, closed loop systems, diseases, patient treatment, drugs, sugarOther keywords: blood glucose concentration control, type 1 diabetic patients, multiple‐model strategy, alternative closed‐loop method, subcutaneous insulin delivery, type 1 diabetes, transfer functions, control objectives, PID controllers, subcutaneous blood glucose measuring, glucose‐dependent setpoint, closed‐loop glucose–insulin regulatory system, closed‐loop mechanism     

Rehabilitation of the Parkinson's tremor by using robust adaptive sliding mode controller: a simulation study

One of the efficient methods in controlling the Parkinson''s tremor is Deep Brain Stimulation (DBS) therapy. The stimulation of Basal Ganglia (BG) by DBS brings no feedback though the existence of feedback reduces the additional stimulatory signal delivered to the brain. So this study offers a new adaptive architecture of a closed‐loop control system in which two areas of BG are stimulated simultaneously to decrease the following three indicators: hand tremor, the level of a delivered stimulation signal in the disease condition, and the level of a delivered stimulation signal in health condition to the disease condition. One area (STN: subthalamic nucleus) is stimulated with an adaptive sliding mode controller and the other area (GPi: Globus Pallidus internal) with partial state feedback controller. The simulation results of stimulating two areas of BG showed satisfactory performance.Inspec keywords: bioelectric phenomena, diseases, variable structure systems, brain models, biomedical electrodes, adaptive control, closed loop systems, state feedback, feedback, neurophysiology, brain, robust controlOther keywords: DBS, additional stimulatory signal, adaptive architecture, closed‐loop control system, hand tremor, delivered stimulation signal, disease condition, health condition, partial state feedback controller, Parkinson''s tremor, robust adaptive sliding mode controller, simulation study, efficient methods, Deep Brain Stimulation therapy, Basal Ganglia     

Parameter estimation of a meal glucose–insulin model for TIDM patients from therapy historical data

The effect of meal on blood glucose concentration is a key issue in diabetes mellitus because its estimation could be very useful in therapy decisions. In the case of type 1 diabetes mellitus (T1DM), the therapy based on automatic insulin delivery requires a closed‐loop control system to maintain euglycaemia even in the postprandial state. Thus, the mathematical modelling of glucose metabolism is relevant to predict the metabolic state of a patient. Moreover, the eating habits are characteristic of each person, so it is of interest that the mathematical models of meal intake allow to personalise the glycaemic state of the patient using therapy historical data, that is, daily measurements of glucose and records of carbohydrate intake and insulin supply. Thus, here, a model of glucose metabolism that includes the effects of meal is analysed in order to establish criteria for data‐based personalisation. The analysis includes the sensitivity and identifiability of the parameters, and the parameter estimation problem was resolved via two algorithms: particle swarm optimisation and evonorm. The results show that the mathematical model can be a useful tool to estimate the glycaemic status of a patient and personalise it according to her/his historical data.Inspec keywords: medical control systems, closed loop systems, particle swarm optimisation, parameter estimation, biochemistry, diseases, patient monitoring, patient diagnosis, blood, sugar, patient treatment, medical computingOther keywords: meal intake, metabolic state, mathematical modelling, postprandial state, closed‐loop control system, automatic insulin delivery, T1DM, type 1 diabetes mellitus, therapy decisions, blood glucose concentration, TIDM patients, meal glucose–insulin model, mathematical model, parameter estimation problem, data‐based personalisation, glucose metabolism, insulin supply, carbohydrate intake, glucose records, therapy historical data, glycaemic state     

Fuzzy controller design for breast cancer treatment based on fractal dimension using breast thermograms

In this study, three non‐linear indices consist of compression, one‐dimensional (1D) and two‐dimensional (2D) fractal dimensions are used for the determination of the malignancy or benignity of cancer tumours in breast thermograms. On the other hand, by developing the high‐precision infrared cameras as well as new methods of image processing, biomedical thermography images have found a prominent position among the others. Furthermore, cancerous tissue can be affected by the laser. In this study, in order to treat the cancerous lesion identified by breast thermograms, the laser parameters are designed. The basis of controller designing is the obtained non‐linear indices. If the indices are moved from the chaotic behaviour to normal condition, the treating tissue is going from cancerous to a healthy condition and the treatment process is completed. Radiation frequency and the energy density of laser are designed as two key elements in the cancer treatment. In this study, the type I and type II fuzzy controllers are employed for the control strategies. Using the proposed closed‐loop control, the non‐linear indices of the cancerous lesion will be reduced during the treatment process. The simulation results on two datasets of breast thermograms indicate the superiority of type II fuzzy controller.Inspec keywords: closed loop systems, fractals, infrared imaging, tumours, fuzzy control, medical image processing, cancer, biological tissues, gynaecology, patient treatmentOther keywords: cancer tumours, breast thermograms, high‐precision infrared cameras, image processing, cancerous tissue, cancerous lesion, nonlinear indices, type II fuzzy controller, closed‐loop control, fuzzy controller design, breast cancer treatment, 2D fractal dimensions     

Controllability and observability analysis of basal ganglia model and feedback linearisation control

Deep brain stimulation (DBS) is a clinical remedy to control tremor in Parkinson''s disease. In DBS, one of the two main areas of basal ganglia (BG) is stimulated. This stimulation is produced with no feedback of the tremor and often causes a wide range of unpleasant side effects. Using a feedback signal from tremor, the stimulatory signal can be reduced or terminated to avoid extra stimulation and as a result decrease the side effects. To design a closed‐loop controller for the non‐linear BG model, a complete study of controllability and observability of this system is presented in this study. This study shows that the BG model is controllable and observable. The authors also propose the idea of stimulating the two BG areas simultaneously. A two‐part controller is then designed: a feedback linearisation controller for subthalamic nucleus stimulation and a partial state feedback controller for globus pallidus internal stimulation. The controllers are designed to decrease three indicators: the hand tremor, the level of delivered stimulation signal in disease condition, and the ratio of the level of delivered stimulation signal in health condition to disease condition. Considering these three indicators, the simulation results show satisfactory performance.Inspec keywords: feedback, brain, neurophysiology, diseases, medical control systems, closed loop systems, controllers, linearisation techniques, bioelectric phenomenaOther keywords: controllability analysis, observability analysis, basal ganglia model, feedback linearisation control, deep brain stimulation, clinical remedy, tremor control, Parkinson''s disease, feedback signal, closed‐loop controller, nonlinear BG model, feedback linearisation controller, two‐part controller, subthalamic nucleus stimulation, partial state feedback controller, globus pallidus internal stimulation, disease condition, delivered stimulation signal     

Non‐linear feedback control of the p 53 protein–mdm 2 inhibitor system using the derivative‐free non‐linear Kalman filter

It is proven that the model of the p 53–mdm 2 protein synthesis loop is a differentially flat one and using a diffeomorphism (change of state variables) that is proposed by differential flatness theory it is shown that the protein synthesis model can be transformed into the canonical (Brunovsky) form. This enables the design of a feedback control law that maintains the concentration of the p 53 protein at the desirable levels. To estimate the non‐measurable elements of the state vector describing the p 53–mdm 2 system dynamics, the derivative‐free non‐linear Kalman filter is used. Moreover, to compensate for modelling uncertainties and external disturbances that affect the p 53–mdm 2 system, the derivative‐free non‐linear Kalman filter is re‐designed as a disturbance observer. The derivative‐free non‐linear Kalman filter consists of the Kalman filter recursion applied on the linearised equivalent of the protein synthesis model together with an inverse transformation based on differential flatness theory that enables to retrieve estimates for the state variables of the initial non‐linear model. The proposed non‐linear feedback control and perturbations compensation method for the p 53–mdm 2 system can result in more efficient chemotherapy schemes where the infusion of medication will be better administered.Inspec keywords: proteins, molecular biophysics, biochemistry, Kalman filters, inverse problems, perturbation theoryOther keywords: nonlinear feedback control, p53 protein‐mdm2 inhibitor system, derivative‐free nonlinear Kalman filter, differential flatness theory, protein synthesis loop, diffeomorphism, protein synthesis model, feedback control law, nonmeasurable elements, modelling uncertainties, inverse transformation, nonlinear model, perturbation compensation method, chemotherapy schemes, medication infusion     

Blood glucose concentration control for type 1 diabetic patients: a non‐linear suboptimal approach

In this study, a closed‐loop treatment strategy is proposed for the control of blood glucose levels in type 1 diabetic patients. Toward this end, a non‐linear technique for designing suboptimal tracking controllers, called the state‐dependent Riccati equation tracker, is used based on a mathematical model of the glucose–insulin regulatory system. Since two state variables of the utilised model are not available to the controller, a non‐linear filter is also designed to estimate these variables using the measured blood glucose concentration. Effects of unannounced meals and regular exercise are investigated for a nominal patient and nine diabetic patients with unknown parameters. Numerical simulations are given to show the effectiveness of the proposed treatment strategy even in the presence of parametric uncertainties and the observation noise.Inspec keywords: blood, biochemistry, diseases, patient treatment, Riccati equations, nonlinear filters, medical signal processingOther keywords: blood glucose concentration control, type 1 diabetic patients, nonlinear suboptimal approach, closed‐loop treatment strategy, suboptimal tracking controllers, state‐dependent Riccati equation tracker, glucose‐insulin regulatory system, nonlinear filter, unannounced meals, regular exercise, treatment strategy, parametric uncertainties, observation noise     

Sliding mode controller–observer pair for p53 pathway

A significant loss of p53 protein, an anti‐tumour agent, is observed in early cancerous cells. Induction of small molecules based drug is by far the most prominent technique to revive and maintain wild‐type p53 to the desired level. In this study, a sliding mode control (SMC) based robust non‐linear technique is presented for the drug design of a control‐oriented p53 model. The control input generated by conventional SMC is discontinuous; however, depending on the physical nature of the system, drug infusion needs to be continuous. Therefore, to obtain a smooth control signal, a dynamic SMC (DSMC) is designed. Moreover, the boundedness of the zero‐dynamics is also proved. To make the model‐based control design possible, the unknown states of the system are estimated using an equivalent control based, reduced‐order sliding mode observer. The robustness of the proposed technique is assessed by introducing input disturbance and parametric uncertainty in the system. The effectiveness of the proposed control scheme is witnessed by performing in‐silico trials, revealing that the sustained level of p53 can be achieved by controlled drug administration. Moreover, a comparative quantitative analysis shows that both controllers yield similar performance. However, DSMC consumes less control energy.Inspec keywords: control system synthesis, tumours, variable structure systems, observers, cancer, robust control, drugs, proteins, medical control systemsOther keywords: wild‐type p53, nonlinear technique, drug design, control‐oriented p53 model, control input, drug infusion, smooth control signal, dynamic SMC, zero‐dynamics, model‐based control design, input disturbance, controlled drug administration, sliding mode controller–observer pair, cancerous cells, antitumour agent, molecule based drug     

Sliding mode control for a fractional‐order non‐linear glucose‐insulin system

By providing the generalisation of integration and differentiation, and incorporating the memory and hereditary effects, fractional‐order modelling has gotten significant attention in the past few years. One of the extensively studied and utilised models to describe the glucose–insulin system of a human body is Bergman''s minimal model. This non‐linear model comprises of integer‐order differential equations. However, comparison with the experimental data shows that the fractional‐order version of Bergman''s minimal model is a better representative of the glucose–insulin system than its original integer‐order model. To design a control law for an artificial pancreas for a diabetic patient using a fractional‐order model, different techniques, including feedback linearisation, have been applied in the literature. The authors’ previous work shows that the fractional‐order version of Bergman''s model describes the glucose–insulin system in a better way than the integer‐order model. This study applies the sliding mode control technique and then compares the obtained simulation results with the ones obtained using feedback linearisation.Inspec keywords: nonlinear control systems, feedback, variable structure systems, differential equations, medical control systems, diseases, control system synthesis, sugar, nonlinear dynamical systemsOther keywords: fractional‐order nonlinear glucose‐insulin, hereditary effects, fractional‐order modelling, extensively, utilised models, glucose–insulin system, Bergman''s minimal model, nonlinear model, integer‐order differential equations, fractional‐order version, original integer‐order model, fractional‐order model, Bergman''s model, sliding mode control technique     

Design of dual hormone blood glucose therapy and comparison with single hormone using MPC algorithm

The complete automated control and delivery of insulin and glucagon in type 1 diabetes is the developing technology for artificial pancreas. This improves the quality of life of a diabetic patient with the precise infusion. The amount of infusion of these hormones is controlled using a control algorithm, which has the prediction property. The control algorithm model predictive control (MPC) predicts one step ahead and infuses the hormones continuously according to the necessity for the regulation of blood glucose. In this research, the authors propose a MPC control algorithm, which is novel for a dual hormone infusion, for a mathematical model such as Sorenson model, and compare it with the insulin alone or single hormone infusion developed with MPC. Since they aim for complete automatic control and regulation, unmeasured disturbances at a random time are integrated and the performance evaluation is projected through statistical analysis. The blood glucose risk index (BGRI) and control variability grid analysis (CVGA) plot gives the additional evaluation for the comparative results of the two controllers claiming 88% performance by dual hormone evaluated through CVGA plot and 2.05 mg/dl average tracking error, 2.20 BGRI. The MPC developed for dual hormone significantly performs better and the time spent in normal glycaemia is longer while eliminating the risk of hyperglycaemia and hypoglycaemia.Inspec keywords: sugar, diseases, statistical analysis, biochemistry, blood, predictive control, patient treatment, artificial organsOther keywords: dual hormone blood glucose therapy, insulin, glucagon, type 1 diabetes, artificial pancreas, diabetic patient, glucose level, model predictive control, MPC control algorithm, dual hormone infusion, single hormone infusion, complete automatic control, blood glucose risk index, control variability grid analysis plot, Sorenson model, statistical analysis, normal glycaemia, hyperglycaemia, hypoglycaemia     

Estimation of parameters for plasma glucose regulation in type‐2 diabetics in presence of meal

In this study, the authors propose a methodology for the estimation of glucose masses in stomach (both in solid and liquid forms), intestine, plasma and tissue; insulin masses in portal vein, liver, plasma and interstitial fluid using only plasma glucose measurement. The proposed methodology fuses glucose–insulin homoeostasis model (in the presence of meal intake) and plasma glucose measurement with a Bayesian non‐linear filter. Uncertainty of the model over individual variations has been incorporated by adding process noise to the homoeostasis model. The estimation is carried out over 24 h for the healthy people as well as a type II diabetes mellitus patients. In simulation, the estimator follows the truth accurately for both the cases. Moreover, the performances of two non‐linear filters, namely the unscented Kalman filter (KF) and cubature quadrature KF are compared in terms of root mean square error. The proposed methodology will be helpful in future to: (i) observe a patient''s insulin–glucose profile, (ii) calculate drug dose for any hyperglycaemic patients and (iii) develop a closed‐loop controller for automated insulin delivery system.Inspec keywords: blood, diseases, biochemistry, parameter estimation, biological tissues, liver, Bayes methods, nonlinear filters, Kalman filters, drugs, drug delivery systems, medical signal processingOther keywords: automated insulin delivery system, closed‐loop controller, hyperglycaemic patients, drug dose, root mean square error, cubature quadrature KF, Kalman filter, type II diabetes mellitus, process noise, Bayesian nonlinear filter, glucose‐insulin homoeostasis model, interstitial fluid, liver, portal vein, insulin mass, biological tissues, intestine, stomach, glucose mass, meal intake, type‐2 diabetics, plasma glucose regulation, parameter estimation     

Architecture‐dependent robustness in a class of multiple positive feedback loops

Many types of multiple positive feedbacks with each having potentials to generate bistability exist extensively in natural, raising the question of why a particular architecture is present in a cell. In this study, the authors investigate multiple positive feedback loops across three classes: one‐loop class, two‐loop class and three‐loop class, where each class is composed of double positive feedback loop (DPFL) or double negative feedback loop (DNFL) or both. Through large‐scale sampling and robustness analysis, the authors find that for a given class, the homogeneous DPFL circuit (i.e. the coupled circuit that is composed of only DPFLs) is more robust than all the other circuits in generating bistable behaviour. In addition, stochastic simulation shows that the low stable state is more robust than the high stable state in homogeneous DPFL whereas the high‐stable state is more robust than the low‐stable state in homogeneous DNFL circuits. It was argued that this investigation provides insight into the relationship between robustness and network architecture.Inspec keywords: cellular biophysics, feedback, sampling methods, stochastic processesOther keywords: network architecture, low stable state, stochastic simulation, bistable behaviour, homogeneous DPFL circuit, robustness analysis, large‐scale sampling, DNFL, double negative feedback loop, double positive feedback loop, three‐loop class, two‐loop class, one‐loop class, cell architecture, bistability, multiple positive feedback loops, architecture‐dependent robustness