Winston Garcia-Gabin

Robust Sliding Mode Closed-loop Glucose Control with Meal Compensation in Type 1 Diabetes Mellitus

Abstract This work addresses the design of a robust closed-loop plasma glucose controller for Type 1 Diabetes Mellitus patients. The feedback controller is based on Sliding Mode Control (SMC) while robust feedforward boluses to compensate food intake are calculated in a robust way by means of an interval glucose predictor that minimizes the risk of hypoglycaemia. The designed controller has been validated in a virtual environment following standard protocols. The resulting control algorithm shows a considerable robustness regarding intra-patient variability in insulin sensitivity as well as an enhanced ability to handle disturbance rejection. The International Diabetes Federation guidelines for glycaemia targets in Diabetes Mellitus are fulfilled by the designed control strategy.

Real-Time Glucose Estimation Algorithm for Continuous Glucose Monitoring Using Autoregressive Models

Background: Continuous glucose monitors (CGMs) present a problem of lack of accuracy, especially in the lower range, sometimes leading to missed or false hypoglycemia. A new algorithm is presented here aimed at improving the measurement accuracy and hypoglycemia detection. Its core is the estimation of blood glucose (BG) in real time (RT) from CGM intensity readings using autoregressive (AR) models. Methods: Eighteen patients with type 1 diabetes were monitored for three days (one at the hospital and two at home) using the CGMS® Gold. For these patients, BG samples were taken every 15 min for 2 h after meals and every half hour otherwise during the first day. The relationship between the current measured by the CGMS Gold and BG was learned by an AR model, allowing its RT estimation. New capillary glucose measurements were used to correct the model BG estimations. Results: A total of 563 paired points were obtained from BG and monitor readings to validate the new algorithm. 98.5% of paired points fell in zones A+B of the Clarke error grid analysis with the proposed algorithm. The overall mean and median relative absolute differences (RADs) were 9.6% and 6.7%. Measurements meeting International Organization for Standardization (ISO) criteria were 88.7%. In the hypoglycemic range, the mean and median RADs were 8.1% and 6.0%, and measurements meeting ISO criteria were 86.7%. The sensitivity and specificity with respect to hypoglycemia detection were 91.5% and 95.0%. Conclusions: The performance measured with both clinical and numerical accuracy metrics illustrates the improved accuracy of the proposed algorithm compared with values presented in the literature. A significant improvement in hypoglycemia detection was also observed.

Internal model sliding mode control approach for glucose regulation in type 1 diabetes

Abstract Patients with type 1 diabetes require insulin therapy to maintain blood glucose levels within safe ranges since their pancreas is unable to complete its function. The development of a closed-loop artificial pancreas capable of maintaining normoglycemia during daily life will dramatically improve the quality of life for insulin-dependent diabetic patients. In this work, a closed-loop control strategy for blood glucose level regulation in type 1 diabetic patients is presented. A robust controller is designed using a combination of internal model and sliding mode control techniques. Also, the controller is provided with a feedforward loop to improve meal compensation. A simulation environment designed for testing the artificial pancreas control algorithms has been used to evaluate the controller. The simulation results show a good controller performance in fasting conditions and meal disturbance rejection, and robustness against model–patient mismatch and meal estimation errors.

A robust sliding mode controller with internal model for closed-loop artificial pancreas

The study presents a robust closed-loop sliding mode controller with internal model for blood glucose control in type-1 diabetes. Type-1 diabetic patients depend on external insulin delivery to keep their blood glucose within near-normal ranges. Closed-loop artificial pancreas is developed to help avoid dangerous, potentially life-threatening hypoglycemia, as well as to prevent complication-inducing hyperglycemia. The proposed controller is designed using a combination of sliding mode and internal model control techniques. To enhance postprandial performance, a feedforward controller is added to inject insulin bolus. Simulation studies have been performed to test the controller, which revealed that the proposed control strategy is able to control the blood glucose well within the safe limits in the presence of meals and measurements errors. The controller shows acceptable robustness against changes in insulin sensitivity, model–patient mismatch, and errors in estimating meal’s contents.

Using Support Vector Machines to Detect Therapeutically Incorrect Measurements by the MiniMed CGMS

Background: Current continuous glucose monitors have limited accuracy mainly in the low range of glucose measurements. This lack of accuracy is a limiting factor in their clinical use and in the development of the so-called artificial pancreas. The ability to detect incorrect readings provided by continuous glucose monitors from raw data and other information supplied by the monitor itself is of utmost clinical importance. In this study, support vector machines (SVMs), a powerful statistical learning technique, were used to detect therapeutically incorrect measurements made by the Medtronic MiniMed CGMS®. Methods: Twenty patients were monitored for three days (first day at the hospital and two days at home) using the MiniMed CGMS. After the third day, the monitor data were downloaded to the physicians computer. During the first 12 hours, the patients stayed in the hospital, and blood samples were taken every 15 minutes for two hours after meals and every 30 minutes otherwise. Plasma glucose measurements were interpolated using a cubic method for time synchronization with simultaneous MiniMed CGMS measurements every five minutes, obtaining a total of 2281 samples. A Gaussian SVM classifier trained on the monitors electrical signal and glucose estimation was tuned and validated using multiple runs of k-fold cross-validation. The classes considered were Clarke error grid zones A+B and C+D+E. Results: After ten runs of ten-fold cross-validation, an average specificity and sensitivity of 92.74% and 75.49%, respectively, were obtained (see Figure 4). The average correct rate was 91.67%. Conclusions: Overall, the SVM performed well, in spite of the somewhat low sensitivity. The classifier was able to detect the time intervals when the monitors glucose profile could not be trusted due to incorrect measurements. As a result, hypoglycemic episodes missed by the monitor were detected.

A solar cooling plant: A benchmark for hybrid systems control

Abstract This paper describes the hybrid model of a solar cooling plant. This model considers all possible operating modes of the process, which are modelled as a finite state machine whose transition conditions are given by the discrete variables. The discrete variables are the electrovalves and pumps. The model has been written as a mixed logical dynamical system and is simulated using Stateflow/Simulink Matlab. The model has been validated using real data from the plant. This plant is being used as a benchmark for hybrid control experiences by many European researchers in the framework of the HYCON Network of Excellence.

Hierarchical Control of a Hybrid Solar Air Conditioning Plant

This paper describes the development and practical application of a hierarchical scheme for the global control of a solar air conditioning plant. The plant is a variable configuration hybrid process characterised by nonlinearities and time delays, which uses two energy sources for its daily operation, namely solar energy and gas. A control algorithm able to handle these characteristics ensures that the plant gets constantly reconfigured for the most suitable operating mode. The envisaged global control encompasses the starting and stopping phases, as well as operating the plant with and without cooling demand. The hierarchical structure proposed is composed of two main levels, namely the configuration and the regulatory control levels. The configuration level selects the operating mode by means of minimizing a linear function with variable weights. The weights assigned depend on the current state of the plant and on the weather conditions, since the main energy source is solar radiation and it directly influences the selected operating mode. The regulatory control level instead adjusts the variables of the process related to each operating mode by using model predictive control in several structures of control loops. We show the results of the implementation of the hierarchical scheme over the real plant. The scheme exhibits a satisfactory behaviour even in presence of adverse weather conditions, thus proving able to satisfy the cooling demand throughout the day.

Wiener sliding-mode control for artificial pancreas: A new nonlinear approach to glucose regulation

Type 1 diabetic patients need insulin therapy to keep their blood glucose close to normal. In this paper an attempt is made to show how nonlinear control-oriented model may be used to improve the performance of closed-loop control of blood glucose in diabetic patients. The nonlinear Wiener model is used as a novel modeling approach to be applied to the glucose control problem. The identified Wiener model is used in the design of a robust nonlinear sliding mode control strategy. Two configurations of the nonlinear controller are tested and compared to a controller designed with a linear model. The controllers are designed in a Smith predictor structure to reduce the effect of system time delay. To improve the meal compensation features, the controllers are provided with a simple feedforward controller to inject an insulin bolus at meal time. Different simulation scenarios have been used to evaluate the proposed controllers. The obtained results show that the new approach outperforms the linear control scheme, and regulates the glucose level within safe limits in the presence of measurement and modeling errors, meal uncertainty and patient variations.

Prediction of glucose excursions under uncertain parameters and food intake in intensive insulin therapy for type 1 diabetes mellitus

Considering the difficulty in the insulin dosage selection and the problem of hyper- and hypoglycaemia episodes in type 1 diabetes, dosage-aid systems appear as tremendously helpful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as the large intra-patient variability and food intake. This work addresses the prediction of glycaemia for a given insulin therapy face to parametric and input uncertainty, by means of modal interval analysis. As result, a band containing all possible glucose excursions suffered by the patient for the given uncertainty is obtained. From it, a safer prediction of possible hyper- and hypoglycaemia episodes can be calculated.

SLIDING MODE PREDICTIVE CONTROL FOR CHEMICAL PROCESS WITH TIME DELAY

Abstract A design of a novel model predictive controller is presented. The proposed Sliding Mode Predictive Control (SMPC) algorithm combines the design technique of Sliding-Mode Control (SMC) with Model based Predictive Control (MPC). The SMPC showed a considerable robustness improvement with respect to MPC in the presence of time delay, and showed an enhanced ability to handle set point changes in a nonlinear process. Its robustness was evaluated using a robustness plot, its performance was judged using a single input single output nonlinear mixing tank process with variable time delay.