An adaptive-learning model predictive control (AL-MPC) framework is proposed for
incorporating disturbance prediction, model uncertainty quantification, pattern learning, and …

This paper presents an approximate Reinforcement Learning (RL) methodology for bi-level
power management of networked Microgrids (MG) in electric distribution systems. In …

Abstract Theory and methods to obtain reduced order models by moment matching from
input/output data are presented. Algorithms for the estimation of the moments of linear and …

An adaptive model predictive control (MPC) algorithm with dynamic adjustments of
constraints and objective function weights based on estimates of the plasma insulin …

Background: Despite the recent advancements in the modeling of glycemic dynamics for
type 1 diabetes mellitus, automatically considering unannounced meals and exercise …

Mathematical models are at the core of modern science and technology. An accurate
description of behaviors, systems and processes often requires the use of complex models …

An adaptive and personalized multivariable artificial pancreas (mAP) system using plasma
insulin estimates is proposed to efficiently accommodate major disturbances to the blood …

Background: Physical activity presents a significant challenge for glycemic control in
individuals with type 1 diabetes. As accurate glycemic predictions are key to successful …

A repetitive control method is presented that is implemented in real-time for periodic wind
disturbance rejection for linear systems with multiple inputs and multiple outputs and with …

Dedicated tip–tilt loops are commonly implemented on adaptive optics (AO) systems. In
addition, a number of recent high-performance systems feature tip–tilt controllers that are …