@booklet {, title = {Neural Simplex Architecture}, year = {2020}, author = {Dung T. Phan and Radu Grosu and Nils Jansen and Nicola Paoletti and Scott A. Smolka and Scott D. Stoller} } @conference {, title = {Neural State Classification for Hybrid Systems}, booktitle = {Proc.\ 16th International Symposium on Automated Technology for Verification and Analysis (ATVA 2018)}, year = {2018}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, author = {Dung Phan and Nicola Paoletti and Timothy Zhang and Radu Grosu and Scott A. Smolka and Scott D. Stoller} } @article {, title = {Closed-loop quantitative verification of rate-adaptive pacemakers}, journal = {ACM Transactions on Cyber-Physical Systems, to appear}, year = {2017}, abstract = {

Cardiac pacemakers are electrical devices that treat arrhythmias delivering electrical stimuli to the patient heart {\textbullet} Rate-adaptation: regulation of pacing rate according to patient{\textquoteright}s needs (e.g. increased pacing rate during exercise) {\textbullet} Programming of rate-adaptation parameters depends on many patient-specific factors (age, lifestyle, tolerance to rapid pacing, {\textellipsis}) {\textbullet} Effective personalisation achievable only through extensive exercise testing: intolerable for a cardiac patient {\textbullet} We introduce a data-driven and model-based approach for subjectspecific verification of rate-adaptive pacemakers

}, author = {Nicola Paoletti and Andrea Patan{\`e} and Marta Kwiatkowska} } @conference {, title = {{Data-Driven Robust Control for Type 1 Diabetes Under Meal and Exercise Uncertainties}}, booktitle = {Computational Methods in Systems Biology}, year = {2017}, publisher = {Springer}, organization = {Springer}, abstract = {

We present a fully closed-loop design for an artificial pancreas (AP) which regulates the delivery of insulin for the control of Type I diabetes. Our AP controller operates in a fully automated fashion, without requiring any manual interaction (e.g. in the form of meal announcements) with the patient. A major obstacle to achieving closed-loop insulin control is the uncertainty in those aspects of a patient{\textquoteright}s daily behavior that significantly affect blood glucose, especially in relation to meals and physical activity. To handle such uncertainties, we develop a data-driven robust model-predictive control framework, where we capture a wide range of individual meal and exercise patterns using uncertainty sets learned from historical data. These sets are then used in the controller and state estimator to achieve automated, precise, and personalized insulin therapy. We provide an extensive in silico evaluation of our robust AP design, demonstrating the potential of this approach, without explicit meal announcements, to support high carbohydrate disturbances and to regulate glucose levels in large clusters of virtual patients learned from population-wide survey data.

}, doi = {10.1007/978-3-319-67471-1_13}, author = {Nicola Paoletti and Kin Sum Liu and Scott A. Smolka and Shan Lin} } @conference {, title = {{SMT-based Synthesis of Safe and Robust PID Controllers for Stochastic Hybrid Systems}}, booktitle = {Haifa Verification Conference, to appear}, year = {2017}, abstract = {

Abstract. We present a new method for the automated synthesis of safe and robust Proportional-Integral-Derivative (PID) controllers for stochastic hybrid systems. Despite their widespread use in industry, no automated method currently exists for deriving a PID controller (or any other type of controller, for that matter) with safety and performance guarantees for such a general class of systems. In particular, we consider hybrid systems with nonlinear dynamics (Lipschitzcontinuous ordinary differential equations) and random parameters, and we synthesize PID controllers such that the resulting closed-loop systems satisfy safety and performance constraints given as probabilistic bounded reachability properties. Our technique leverages SMT solvers over the reals and nonlinear differential equations to provide formal guarantees that the synthesized controllers satisfy such properties. These controllers are also robust by design since they minimize the probability of reaching an unsafe state in the presence of random disturbances. We apply our approach to the problem of insulin regulation for type 1 diabetes, synthesizing controllers with robust responses to large random meal disturbances, thereby enabling them to maintain blood glucose levels within healthy, safe ranges.

}, author = {Fedor Shmarov and Nicola Paoletti and Ezio Bartocci and Shan Lin and Scott Smolka and Paolo Zuliani} }