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Abstract-This paper presents a control strategy for blood glucose (BG) level regulation in type 1 diabetic patients. To design the controller, model-based predictive control scheme has been applied to a newly developed diabetic patient model. The controller is provided with a feedforward loop to improve meal compensation, a gain-scheduling scheme to account for different BG levels, and an asymmetric cost function to reduce hypoglycemic risk. A simulation environment that has been approved for testing of artificial pancreas control algorithms has been used to test the controller. The simulation results show a good controller performance in fasting conditions and meal disturbance rejection, and robustness against model-patient mismatch and errors in meal estimation.Index Terms-Gain scheduling (GS), model predictive control (MPC), type 1 diabetes mellitus (T1DM).

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Motor imagery (MI) based brain computer interfaces (BCI) detect changes in brain activity associated with imaginary limb movements, and translate them into device commands. MI based BCIs require training, during which the user gradually learns how to control his or her brain activity with the help of feedback. Additionally, machine learning techniques are frequently used to boost BCI performance and to adapt the decoding algorithm to the user's brain. Thus, both the brain and the machine need to adapt in order to improve performance. To study the utility of co-adaptive training in the BCI paradigm and the time scales involved, we investigated the performance of two groups of subjects, in a 4-day MI experiment using EEG recordings. One group (control, n = 9 subjects) performed the BCI task using a fixed classifier based on MI data from day 1. In the second group (experimental, n = 9 subjects), the classifier was regularly adapted based on brain activity patterns during the experiment days. We found that the experimental group showed a significantly larger change in performance following training compared to the control group. Specifically, although the experimental group exhibited a decrease in performance between days, it showed an increase in performance within each day, which compensated for the decrease. The control group showed decreases both within and between days. A correlation analysis in subjects who had a notable improvement in performance following training showed that performance was mainly associated with modulation of power in the α frequency band. To conclude, continuous updating of the classification algorithm improves the performance of subjects in longitudinal BCI training.

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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.