Observer based nonlinear control design for glucose regulation in type 1 diabetic patients: An LMI approach
TL;DR: The proposed controller can deliver robust closed-loop response of BGC within a specified range of parametric uncertainty and meal disturbances owing to the appropriately tuned bound of LMI region parameters.
About: This article is published in Biomedical Signal Processing and Control.The article was published on 2019-01-01. It has received 47 citations till now. The article focuses on the topics: Nonlinear control & Observer (quantum physics).
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TL;DR: The results of the paper clearly show the superiority of the proposed T2 fuzzy logic control system that is employed to regulate the glucose level in type-1 diabetes.
51 citations
TL;DR: Simulation results are obtained with a single patient testing parameters uncertainties and verify the advantages of the proposed robust control technique in dealing with the effects of external meal disturbance and maintaining the blood glucose concentration in the desired region to avoid the hypoglycemia and hyperglycemia disorders.
28 citations
TL;DR: A historical review of the proposed control algorithms, from the establishment of the paradigm of artificial pancreas to the present date, shows that the main problem to solve remains as the regulation of blood glucose into the healthy physiological range.
22 citations
TL;DR: A robust observer-based adaptive controller for an intravenous glucose tolerance test (IVGTT) model of Type 1 Diabetes Mellitus (T1DM) patients is designed, combining robustness and adaptive philosophy for the first time.
Abstract: The objective of this paper is to design a robust observer-based adaptive controller for an intravenous glucose tolerance test (IVGTT) model of Type 1 Diabetes Mellitus (T1DM) patients. The model i...
22 citations
Cites methods from "Observer based nonlinear control de..."
...An observer-based feedback linearisation applied to the BMM is proposed in Nath et al. (2019)....
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16 May 2021
TL;DR: In this paper, the observer-based state feedback stabilizer design for a class of chaotic systems in the existence of external perturbations and Lipchitz nonlinearities is presented.
Abstract: In this study, the observer-based state feedback stabilizer design for a class of chaotic systems in the existence of external perturbations and Lipchitz nonlinearities is presented. This manuscript aims to design a state feedback controller based on a state observer by the linear matrix inequality method. The conditions of linear matrix inequality guarantee the asymptotical stability of the system based on the Lyapunov theorem. The stabilizer and observer parameters are obtained using linear matrix inequalities, which make the state errors converge to the origin. The effects of the nonlinear Lipschitz perturbation and external disturbances on the system stability are then reduced. Moreover, the stabilizer and observer design techniques are investigated for the nonlinear systems with an output nonlinear function. The main advantages of the suggested approach are the convergence of estimation errors to zero, the Lyapunov stability of the closed-loop system and the elimination of the effects of perturbation and nonlinearities. Furthermore, numerical examples are used to illustrate the accuracy and reliability of the proposed approaches.
19 citations
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04 Feb 2008
TL;DR: In this article, the authors present a method for setting up Uncertainty and Sensitivity Analyses using Monte Carlo and Linear Regression (MCF) models and a set of experiments.
Abstract: Preface. 1. Introduction to Sensitivity Analysi. 1.1 Models and Sensitivity Analysis. 1.1.1 Definition. 1.1.2 Models. 1.1.3 Models and Uncertainty. 1.1.4 How to Set Up Uncertainty and Sensitivity Analyses. 1.1.5 Implications for Model Quality. 1.2 Methods and Settings for Sensitivity Analysis - An Introduction. 1.2.1 Local versus Global. 1.2.2 A Test Model. 1.2.3 Scatterplots versus Derivatives. 1.2.4 Sigma-normalized Derivatives. 1.2.5 Monte Carlo and Linear Regression. 1.2.6 Conditional Variances - First Path. 1.2.7 Conditional Variances - Second Path. 1.2.8 Application to Model (1.3). 1.2.9 A First Setting: 'Factor Prioritization' 1.2.10 Nonadditive Models. 1.2.11 Higher-order Sensitivity Indices. 1.2.12 Total Effects. 1.2.13 A Second Setting: 'Factor Fixing'. 1.2.14 Rationale for Sensitivity Analysis. 1.2.15 Treating Sets. 1.2.16 Further Methods. 1.2.17 Elementary Effect Test. 1.2.18 Monte Carlo Filtering. 1.3 Nonindependent Input Factors. 1.4 Possible Pitfalls for a Sensitivity Analysis. 1.5 Concluding Remarks. 1.6 Exercises. 1.7 Answers. 1.8 Additional Exercises. 1.9 Solutions to Additional Exercises. 2. Experimental Designs. 2.1 Introduction. 2.2 Dependency on a Single Parameter. 2.3 Sensitivity Analysis of a Single Parameter. 2.3.1 Random Values. 2.3.2 Stratified Sampling. 2.3.3 Mean and Variance Estimates for Stratified Sampling. 2.4 Sensitivity Analysis of Multiple Parameters. 2.4.1 Linear Models. 2.4.2 One-at-a-time (OAT) Sampling. 2.4.3 Limits on the Number of Influential Parameters. 2.4.4 Fractional Factorial Sampling. 2.4.5 Latin Hypercube Sampling. 2.4.6 Multivariate Stratified Sampling. 2.4.7 Quasi-random Sampling with Low-discrepancy Sequences. 2.5 Group Sampling. 2.6 Exercises. 2.7 Exercise Solutions. 3. Elementary Effects Method. 3.1 Introduction. 3.2 The Elementary Effects Method. 3.3 The Sampling Strategy and its Optimization. 3.4 The Computation of the Sensitivity Measures. 3.5 Working with Groups. 3.6 The EE Method Step by Step. 3.7 Conclusions. 3.8 Exercises. 3.9 Solutions. 4. Variance-based Methods. 4.1 Different Tests for Different Settings. 4.2 Why Variance? 4.3 Variance-based Methods. A Brief History. 4.4 Interaction Effects. 4.5 Total Effects. 4.6 How to Compute the Sensitivity Indices. 4.7 FAST and Random Balance Designs. 4.8 Putting the Method to Work: the Infection Dynamics Model. 4.9 Caveats. 4.10 Exercises. 5. Factor Mapping and Metamodelling. 5.1 Introduction. 5.2 Monte Carlo Filtering (MCF). 5.2.1 Implementation of Monte Carlo Filtering. 5.2.2 Pros and Cons. 5.2.3 Exercises. 5.2.4 Solutions. 5.2.5 Examples. 5.3 Metamodelling and the High-Dimensional Model Representation. 5.3.1 Estimating HDMRs and Metamodels. 5.3.2 A Simple Example. 5.3.3 Another Simple Example. 5.3.4 Exercises. 5.3.5 Solutions to Exercises. 5.4 Conclusions. 6. Sensitivity Analysis: from Theory to Practice. 6.1 Example 1: a Composite Indicator. 6.1.1 Setting the Problem. 6.1.2 A Composite Indicator Measuring Countries' Performance in Environmental Sustainability. 6.1.3 Selecting the Sensitivity Analysis Method. 6.1.4 The Sensitivity Analysis Experiment and its Results. 6.1.5 Conclusions. 6.2 Example 2: Importance of Jumps in Pricing Options. 6.2.1 Setting the Problem. 6.2.2 The Heston Stochastic Volatility Model with Jumps. 6.2.3 Selecting a Suitable Sensitivity Analysis Method. 6.2.4 The Sensitivity Analysis Experiment. 6.2.5 Conclusions. 6.3 Example 3: a Chemical Reactor. 6.3.1 Setting the Problem. 6.3.2 Thermal Runaway Analysis of a Batch Reactor. 6.3.3 Selecting the Sensitivity Analysis Method. 6.3.4 The Sensitivity Analysis Experiment and its Results. 6.3.5 Conclusions. 6.4 Example 4: a Mixed Uncertainty-Sensitivity Plot. 6.4.1 In Brief. 6.5 When to use What? Afterword. Bibliography. Index.
4,306 citations
TL;DR: Adapt nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.
Abstract: A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L(-1) per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.
1,164 citations
TL;DR: A new simulation model in normal humans that describes the physiological events that occur after a meal, by employing the quantitative knowledge that has become available in recent years, is presented.
Abstract: A simulation model of the glucose-insulin system in the postprandial state can be useful in several circumstances, including testing of glucose sensors, insulin infusion algorithms and decision support systems for diabetes. Here, we present a new simulation model in normal humans that describes the physiological events that occur after a meal, by employing the quantitative knowledge that has become available in recent years. Model parameters were set to fit the mean data of a large normal subject database that underwent a triple tracer meal protocol which provided quasi-model-independent estimates of major glucose and insulin fluxes, e.g., meal rate of appearance, endogenous glucose production, utilization of glucose, insulin secretion. By decomposing the system into subsystems, we have developed parametric models of each subsystem by using a forcing function strategy. Model results are shown in describing both a single meal and normal daily life (breakfast, lunch, dinner) in normal. The same strategy is also applied on a smaller database for extending the model to type 2 diabetes.
856 citations
TL;DR: Discusses analysis and synthesis techniques for robust pole placement in linear matrix inequality (LMI) regions, a class of convex regions of the complex plane that embraces most practically useful stability regions, and describes the effectiveness of this robust pole clustering technique.
Abstract: Discusses analysis and synthesis techniques for robust pole placement in linear matrix inequality (LMI) regions, a class of convex regions of the complex plane that embraces most practically useful stability regions. The focus is on linear systems with static uncertainty on the state matrix. For this class of uncertain systems, the notion of quadratic stability and the related robustness analysis tests are generalized to arbitrary LMI regions. The resulting tests for robust pole clustering are all numerically tractable because they involve solving linear matrix inequalities (LMIs) and cover both unstructured and parameter uncertainty. These analysis results are then applied to the synthesis of dynamic output-feedback controllers that robustly assign the closed-loop poles in a prescribed LMI region. With some conservatism, this problem is again tractable via LMI optimization. In addition, robust pole placement can be combined with other control objectives, such as H/sub 2/ or H/sub /spl infin// performance, to capture realistic sets of design specifications. Physically motivated examples demonstrate the effectiveness of this robust pole clustering technique.
743 citations
TL;DR: Closed-loop glucose control using an external sensor and insulin pump provides a means to achieve near-normal glucose concentrations in youth with type 1 diabetes during the overnight period and the addition of small manual priming bolus doses of insulin improves postprandial glycemic excursions.
Abstract: OBJECTIVE —The most promising β-cell replacement therapy for children with type 1 diabetes is a closed-loop artificial pancreas incorporating continuous glucose sensors and insulin pumps. The Medtronic MiniMed external physiological insulin delivery (ePID) system combines an external pump and sensor with a variable insulin infusion rate algorithm designed to emulate the physiological characteristics of the β-cell. However, delays in insulin absorption associated with the subcutaneous route of delivery inevitably lead to large postprandial glucose excursions.
RESEARCH DESIGN AND METHODS —We studied the feasibility of the Medtronic ePID system in youth with type 1 diabetes and hypothesized that small manual premeal “priming” boluses would reduce postprandial excursions during closed-loop control. Seventeen adolescents (aged 15.9 ± 1.6 years; A1C 7.1 ± 0.8%) underwent 34 h of closed-loop control; 8 with full closed-loop (FCL) control and 9 with hybrid closed-loop (HCL) control (premeal priming bolus).
RESULTS —Mean glucose levels were 135 ± 45 mg/dl in the HCL group versus 141 ± 55 mg/dl in the FCL group ( P = 0.09); daytime glucose levels averaged 149 ± 47 mg/dl in the HCL group versus 159 ± 59 mg/dl in the FCL group ( P = 0.03). Peak postprandial glucose levels averaged 194 ± 47 mg/dl in the HCL group versus 226 ± 51 mg/dl in the FCL group ( P = 0.04). Nighttime control was similar in both groups (111 ± 27 vs. 112 ± 28 mg/dl).
CONCLUSIONS —Closed-loop glucose control using an external sensor and insulin pump provides a means to achieve near-normal glucose concentrations in youth with type 1 diabetes during the overnight period. The addition of small manual priming bolus doses of insulin, given 15 min before meals, improves postprandial glycemic excursions.
527 citations