Projects

SIDDARTA will introduce a new theoretical approach for data-driven control of complex interconnected systems, aiming at parsimony and sustainability, which are crucial challenges in many modern technological applications. Our new vision prioritizes resource frugality over performance in physical systems, seeking an optimal tradeoff between the two by allowing for uncertainties. In turn, uncertainties are mitigated by handling them across scales in a holistic fashion, finding inspiration from recent research endeavors in control and in statistical physics. Our new algorithms will address the massive paradigm shift towards data-driven control of complex systems, and propose sustainable alternatives to the current trend of evermore resource-voracious methods.

Data-driven control enables the synthesis of feedback controllers directly from historical data generated by physical systems, and thus eludes the need to construct or identify a model for the underlying system to control. Data-driven approaches are especially useful in scenarios where first-principle models are difficult to derive or the identification task may lead to numerically-unreliable models. In these cases, data-driven methods are preferable since controllers can be synthesized directly from data. The goal of this research line is to push forward the available theory on data-driven control by borrowing tools from machine learning. Robotics is a particular application of interest.

In many control problems, it is desirable to regulate physical systems to optimal operating points and simultaneously recompute these setpoints during runtime accounting for several optimality requirements. To this aim, the goal of this line of research is to develop new control algorithms by drawing inspiration from optimization methods.

Traffic networks are fundamental components of modern societies, making economic activity possible in any city worldwide. This line of research focuses on the development of new control strategies to enable the seamless integration between future electric grids, electric vehicles, and smart buildings with the ultimate goal of supporting the adoption of renewable resources, reducing energy waste, and overall promoting a sustainable shift towards using independent and renewable energy sources.

Mobile robots are used in a broad range of operations thanks to their autonomous capabilities, flexibility, and precision. In this line of research, we are concerned with the problem of making robotic navigation secure, by anticipating and preventing malicious attacks targeting robots’ sensors and actuators.

The number of high-threat infectious diseases continues to rise: some of these are new and some others are re-emerging. Control plays an increasingly central role in the containment of epidemic spreads, as several variables such as mask-wearing, business closures, vaccinations, etc. can be controlled by policymakers. To this end, we work on the development of novel control methods for data-driven decision-making that can guide policymakers in the containment of outbreaks.