The artificial pancreas aims to automate treatment of type 1 diabetes (T1D) by integrating insulin pump and glucose sensor through control algorithms. However, fully closed-loop therapy is challenging since the blood glucose levels to control depend on disturbances related to the patient behavior, mainly meals and physical activity.

To handle meal and exercise uncertainties, in our work we construct data-driven models of meal and exercise behavior, and develop a robust model-predictive control (MPC) system able to reject such uncertainties, in this way eliminating the need for meal announcements by the patient. The data-driven models, called uncertainty sets, are built from data so that they cover the underlying (unknown) distribution with prescribed probabilistic guarantees. Then, our robust MPC system computes the insulin therapy that minimizes the worst-case performance with respect to these uncertainty sets, so providing a principled way to deal with uncertainty. State estimation follows a similar principle to MPC and exploits a prediction model to find the most likely state and disturbance estimate given the observations.

We evaluate our design on synthetic scenarios, including high-carbs intake and unexpected meal delays, and on large clusters of virtual patients learned from population-wide survey data sets (CDC NHANES).

We present a new method for the automated synthesis of digital controllers with formal safety guarantees for systems with nonlinear dynamics, noisy output measurements, and stochastic disturbances. Our method derives digital controllers such that the corresponding closed-loop system, modeled as a sampled-data stochastic control system, satisfies a safety specification with probability above a given threshold. The method alternates between two steps: generation of a candidate controller (sub-optimal but rapid to generate), and verification of the candidate. The candidate is found by maximizing a Monte Carlo estimate of the safety probability, and by simulating the system with a non-validated ODE solver. To rule out unstable candidate controllers, we prove and utilize Lyapunov indirect method for instability of sampled-data nonlinear systems. In the verification step, we use a validated solver based on SMT to compute a numerically and statistically valid confidence interval for the safety probability of the candidate controller. If such probability is not above the threshold, we expand the search space for candidates by increasing the controller degree. We evaluate our technique on three case studies: an artificial pancreas model, a powertrain control model, and a quadruple-tank process.

We introduce Committed Moving Horizon Estimation (CHME), a model-based technique for detecting and estimating unknown random disturbances in control systems. We investigate CHME in the context of meal detection and estimation method for the treatment of type 1 diabetes, where we are interested in automatically detecting the occurrence and estimate the amount of carbohydrate (CHO) intake from glucose sensor data. Meal detection and estimation is crucial in closed-loop insulin control as it can eliminate the need for manual meal announcements by the patient.

CMHE extends Moving Horizon Estimation, which alone, is not well-suited for disturbance estimation and meal detection. Indeed, accurate disturbance estimation needs both look-ahead (awareness of the disturbance effect on future system outputs) and history (past outputs to discriminate the start of the disturbance). For this purpose, CMHE aggregates the meal disturbances estimated by multiple MHE instances to balance future and past information at decision time, thus providing timely detection and accurate estimation. Applied to the detection of random meals from glucose sensor data, our method achieves an 88.5% overall detection rate and a 100% detection rate for the main meals (i.e., excluding snacks).